dguiducci/Skald-Circle
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

First Version

Browse Source
This commit is contained in:
xavix-yo
2026-07-10 15:02:09 +01:00
commit 38494a85a9
562 changed files with 196313 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skills/ics2json/SKILL.md
+75
View File
@@ -0,0 +1,75 @@
---
name: ics2json
description: Download an iCalendar (ICS) feed from a URL and output structured JSON with all events. Use this skill whenever the user needs to read, analyze, or integrate events from a public iCal feed such as Teamup or Google Calendar, especially when no API keys are available. Also use as the base for cron jobs that periodically analyze a calendar feed. If the user mentions an .ics file or calendar feed, use this skill.
---
# ics2json
_Updated: 2026-06-08_
Downloads an iCalendar (ICS) feed from a URL and outputs structured JSON with all events.
## When to use
- Reading and analyzing events from a public iCal feed (Teamup, Google Calendar, etc.)
- Integrating an external calendar without API keys
- As the base for cron jobs that periodically analyze a feed
## Script
`python3 skills/ics2json/ics2json.py <url> [options]`
### Options
| Flag | Description |
|------|-------------|
| `--days N` | Only events starting within the next N days |
| `--all` | Include past events (default: future or ongoing only) |
| `--pretty` | Pretty-print JSON output (default: compact, single-line) |
| `--meta` | Only output calendar metadata (name, description, event count). No events returned. |
### Output JSON format
```json
{
"calendar": "Calendar name",
"description": "Calendar description",
"feed_url": "Feed URL",
"fetched_at": "2026-05-20T12:00:00+01:00",
"total_events": 5,
"events": [
{
"uid": "TU123456",
"title": "Event title",
"description": "Description...",
"location": "Venue",
"where": "Teamup address",
"categories": "kink oriented event ⛓️",
"event_url": "https://teamup.com/...",
"external_url": "https://tickets.example.com",
"start": "2026-05-20T19:00:00+01:00",
"end": "2026-05-20T23:00:00+01:00",
"created": "2026-05-01T10:00:00+00:00",
"last_modified": null,
"stamp": "2026-05-19T13:18:47+00:00",
"attachments": [{"url": "https://...", "type": "image/jpeg", "filename": "flyer.jpg"}]
}
]
}
```
### Examples
```bash
# Download a Teamup feed, future events only (compact output)
python3 skills/ics2json/ics2json.py https://ics.teamup.com/feed/ksmt7zqvai72zisjo4/12645979.ics
# Metadata only (compact)
python3 skills/ics2json/ics2json.py https://ics.teamup.com/feed/ksmt7zqvai72zisjo4/12645979.ics --meta
# Only the next 30 days (compact)
python3 skills/ics2json/ics2json.py https://ics.teamup.com/feed/ksmt7zqvai72zisjo4/12645979.ics --days 30
# All events (including past), pretty-printed for human reading
python3 skills/ics2json/ics2json.py https://ics.teamup.com/feed/ksmt7zqvai72zisjo4/12645979.ics --all --pretty
```
skills/ics2json/ics2json.py
+180
View File
@@ -0,0 +1,180 @@
#!/usr/bin/env python3
"""Download an iCal feed and output events as JSON."""
import argparse
import json
import sys
from datetime import datetime, timedelta, timezone
from typing import Any, Dict, List, Optional
from urllib.request import urlopen
from icalendar import Calendar
def parse_dt(value: Any) -> Optional[str]:
"""Parse an iCal date/datetime value and return ISO 8601 string."""
if value is None:
return None
dt = value.dt
if isinstance(dt, datetime):
if dt.tzinfo is None:
dt = dt.replace(tzinfo=timezone.utc)
return dt.isoformat()
# date-only (all-day events)
return dt.isoformat()
def extract_attachments(event: Any) -> List[Dict[str, str]]:
"""Extract ATTACH properties as a list of {url, type} dicts."""
attachments: List[Dict[str, str]] = []
if "ATTACH" in event:
# May be a single value or a list
items = event["ATTACH"] if isinstance(event["ATTACH"], list) else [event["ATTACH"]]
for item in items:
attach: Dict[str, str] = {"url": str(item)}
params = item.params
if "FMTTYPE" in params:
attach["type"] = params["FMTTYPE"]
if "FILENAME" in params:
attach["filename"] = params["FILENAME"]
attachments.append(attach)
return attachments
def extract_text(component: Any, key: str) -> Optional[str]:
"""Extract a text property, decoded from any encoding."""
raw = component.get(key)
if raw is None:
return None
# vCategory objects have a .cats attribute (list of vText)
if hasattr(raw, "cats"):
return ", ".join(str(item) for item in raw.cats)
# Other list-like properties
if isinstance(raw, list):
return ", ".join(str(item) for item in raw)
return str(raw)
def event_to_dict(event: Any) -> Dict[str, Any]:
"""Convert an iCal VEVENT to a flat dict."""
return {
"uid": extract_text(event, "UID"),
"title": extract_text(event, "SUMMARY"),
"description": extract_text(event, "DESCRIPTION"),
"location": extract_text(event, "LOCATION"),
"where": extract_text(event, "X-TEAMUP-WHERE"),
"categories": extract_text(event, "CATEGORIES"),
"event_url": extract_text(event, "URL"),
"external_url": extract_text(event, "X-TEAMUP-EVENT-URL"),
"start": parse_dt(event.get("DTSTART")),
"end": parse_dt(event.get("DTEND")),
"created": parse_dt(event.get("CREATED")),
"last_modified": parse_dt(event.get("LAST-MODIFIED")),
"stamp": parse_dt(event.get("DTSTAMP")),
"attachments": extract_attachments(event),
}
def download_feed(url: str) -> Calendar:
"""Download and parse an iCal feed."""
with urlopen(url) as response:
raw = response.read()
return Calendar.from_ical(raw)
def _parse_iso(iso: str) -> datetime:
"""Parse an ISO 8601 string, making it UTC-aware."""
dt = datetime.fromisoformat(iso)
if dt.tzinfo is None:
dt = dt.replace(tzinfo=timezone.utc)
return dt
def is_future(event_dict: Dict[str, Any], now: datetime) -> bool:
"""Check if an event is in the future (or ongoing)."""
end = event_dict.get("end")
if end is None:
start = event_dict.get("start")
if start is None:
return True
return _parse_iso(start) >= now
return _parse_iso(end) >= now
def is_within_days(event_dict: Dict[str, Any], days: int, now: datetime) -> bool:
"""Check if an event starts within the next N days."""
start = event_dict.get("start")
if start is None:
return True
dt = _parse_iso(start)
cutoff = now + timedelta(days=days)
return dt >= now and dt <= cutoff
def main() -> None:
parser = argparse.ArgumentParser(description="Download an iCal feed and output events as JSON")
parser.add_argument("url", help="URL of the iCal feed (.ics)")
parser.add_argument("--days", type=int, default=None, help="Only return events starting within the next N days")
parser.add_argument("--all", action="store_true", help="Include past events (default: future only)")
parser.add_argument("--pretty", action="store_true", help="Pretty-print JSON output (default: compact)")
parser.add_argument("--meta", action="store_true", help="Only output calendar metadata (no events)")
args = parser.parse_args()
try:
cal = download_feed(args.url)
except Exception as e:
print(f"Error downloading feed: {e}", file=sys.stderr)
sys.exit(1)
now = datetime.now(timezone.utc)
# Calendar-level metadata
cal_name = extract_text(cal, "X-WR-CALNAME") or extract_text(cal, "SUMMARY") or "Unknown"
cal_desc = extract_text(cal, "X-WR-CALDESC") or extract_text(cal, "DESCRIPTION") or ""
# Count total events (all of them, unfiltered)
total_all = sum(1 for c in cal.walk() if c.name == "VEVENT")
output: Dict[str, Any] = {
"calendar": cal_name,
"description": cal_desc,
"feed_url": args.url,
"fetched_at": now.isoformat(),
"total_events": total_all,
}
if args.meta:
if args.pretty:
print(json.dumps(output, indent=2, ensure_ascii=False))
else:
print(json.dumps(output, ensure_ascii=False))
return
events: List[Dict[str, Any]] = []
for component in cal.walk():
if component.name != "VEVENT":
continue
ev = event_to_dict(component)
# Apply filters
if not args.all and not is_future(ev, now):
continue
if args.days is not None and not is_within_days(ev, args.days, now):
continue
events.append(ev)
# Sort by start date (ascending, None at the end)
events.sort(key=lambda e: e.get("start") or "9999")
output["total_events"] = len(events)
output["events"] = events
if args.pretty:
print(json.dumps(output, indent=2, ensure_ascii=False))
else:
print(json.dumps(output, ensure_ascii=False))
if __name__ == "__main__":
main()
skills/index.md
+15
View File
@@ -0,0 +1,15 @@
# Skills Index
Skills are reusable capability packages. Each skill lives in its own directory and contains:
- `SKILL.md` — what the skill does, when to use it, and how to invoke its scripts
- one or more Python scripts that perform the actual work
Read `SKILL.md` before running any script. The file explains inputs, outputs, and usage examples.
## Registered Skills
| ics2json | [SKILL.md](ics2json/SKILL.md) | Download an iCal feed and output events as JSON |
| mcp-builder | [SKILL.md](mcp-builder/SKILL.md) | Complete guide + scripts for building high-quality MCP servers (Python FastMCP / TypeScript SDK) |
| skill-creator | [SKILL.md](skill-creator/SKILL.md) | Framework for creating, validating, evaluating and packaging skills |
skills/mcp-builder/LICENSE.txt
+202
View File
@@ -0,0 +1,202 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright 2026 Anthropic, PBC.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
skills/mcp-builder/SKILL.md
+236
View File
@@ -0,0 +1,236 @@
---
name: mcp-builder
description: Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
license: Complete terms in LICENSE.txt
---
# MCP Server Development Guide
## Overview
Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.
---
# Process
## 🚀 High-Level Workflow
Creating a high-quality MCP server involves four main phases:
### Phase 1: Deep Research and Planning
#### 1.1 Understand Modern MCP Design
**API Coverage vs. Workflow Tools:**
Balance comprehensive API endpoint coverage with specialized workflow tools. Workflow tools can be more convenient for specific tasks, while comprehensive coverage gives agents flexibility to compose operations. Performance varies by client—some clients benefit from code execution that combines basic tools, while others work better with higher-level workflows. When uncertain, prioritize comprehensive API coverage.
**Tool Naming and Discoverability:**
Clear, descriptive tool names help agents find the right tools quickly. Use consistent prefixes (e.g., `github_create_issue`, `github_list_repos`) and action-oriented naming.
**Context Management:**
Agents benefit from concise tool descriptions and the ability to filter/paginate results. Design tools that return focused, relevant data. Some clients support code execution which can help agents filter and process data efficiently.
**Actionable Error Messages:**
Error messages should guide agents toward solutions with specific suggestions and next steps.
#### 1.2 Study MCP Protocol Documentation
**Navigate the MCP specification:**
Start with the sitemap to find relevant pages: `https://modelcontextprotocol.io/sitemap.xml`
Then fetch specific pages with `.md` suffix for markdown format (e.g., `https://modelcontextprotocol.io/specification/draft.md`).
Key pages to review:
- Specification overview and architecture
- Transport mechanisms (streamable HTTP, stdio)
- Tool, resource, and prompt definitions
#### 1.3 Study Framework Documentation
**Recommended stack:**
- **Language**: TypeScript (high-quality SDK support and good compatibility in many execution environments e.g. MCPB. Plus AI models are good at generating TypeScript code, benefiting from its broad usage, static typing and good linting tools)
- **Transport**: Streamable HTTP for remote servers, using stateless JSON (simpler to scale and maintain, as opposed to stateful sessions and streaming responses). stdio for local servers.
**Load framework documentation:**
- **MCP Best Practices**: [📋 View Best Practices](./reference/mcp_best_practices.md) - Core guidelines
**For TypeScript (recommended):**
- **TypeScript SDK**: Use WebFetch to load `https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md`
- [⚡ TypeScript Guide](./reference/node_mcp_server.md) - TypeScript patterns and examples
**For Python:**
- **Python SDK**: Use WebFetch to load `https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md`
- [🐍 Python Guide](./reference/python_mcp_server.md) - Python patterns and examples
#### 1.4 Plan Your Implementation
**Understand the API:**
Review the service's API documentation to identify key endpoints, authentication requirements, and data models. Use web search and WebFetch as needed.
**Tool Selection:**
Prioritize comprehensive API coverage. List endpoints to implement, starting with the most common operations.
---
### Phase 2: Implementation
#### 2.1 Set Up Project Structure
See language-specific guides for project setup:
- [⚡ TypeScript Guide](./reference/node_mcp_server.md) - Project structure, package.json, tsconfig.json
- [🐍 Python Guide](./reference/python_mcp_server.md) - Module organization, dependencies
#### 2.2 Implement Core Infrastructure
Create shared utilities:
- API client with authentication
- Error handling helpers
- Response formatting (JSON/Markdown)
- Pagination support
#### 2.3 Implement Tools
For each tool:
**Input Schema:**
- Use Zod (TypeScript) or Pydantic (Python)
- Include constraints and clear descriptions
- Add examples in field descriptions
**Output Schema:**
- Define `outputSchema` where possible for structured data
- Use `structuredContent` in tool responses (TypeScript SDK feature)
- Helps clients understand and process tool outputs
**Tool Description:**
- Concise summary of functionality
- Parameter descriptions
- Return type schema
**Implementation:**
- Async/await for I/O operations
- Proper error handling with actionable messages
- Support pagination where applicable
- Return both text content and structured data when using modern SDKs
**Annotations:**
- `readOnlyHint`: true/false
- `destructiveHint`: true/false
- `idempotentHint`: true/false
- `openWorldHint`: true/false
---
### Phase 3: Review and Test
#### 3.1 Code Quality
Review for:
- No duplicated code (DRY principle)
- Consistent error handling
- Full type coverage
- Clear tool descriptions
#### 3.2 Build and Test
**TypeScript:**
- Run `npm run build` to verify compilation
- Test with MCP Inspector: `npx @modelcontextprotocol/inspector`
**Python:**
- Verify syntax: `python -m py_compile your_server.py`
- Test with MCP Inspector
See language-specific guides for detailed testing approaches and quality checklists.
---
### Phase 4: Create Evaluations
After implementing your MCP server, create comprehensive evaluations to test its effectiveness.
**Load [✅ Evaluation Guide](./reference/evaluation.md) for complete evaluation guidelines.**
#### 4.1 Understand Evaluation Purpose
Use evaluations to test whether LLMs can effectively use your MCP server to answer realistic, complex questions.
#### 4.2 Create 10 Evaluation Questions
To create effective evaluations, follow the process outlined in the evaluation guide:
1. **Tool Inspection**: List available tools and understand their capabilities
2. **Content Exploration**: Use READ-ONLY operations to explore available data
3. **Question Generation**: Create 10 complex, realistic questions
4. **Answer Verification**: Solve each question yourself to verify answers
#### 4.3 Evaluation Requirements
Ensure each question is:
- **Independent**: Not dependent on other questions
- **Read-only**: Only non-destructive operations required
- **Complex**: Requiring multiple tool calls and deep exploration
- **Realistic**: Based on real use cases humans would care about
- **Verifiable**: Single, clear answer that can be verified by string comparison
- **Stable**: Answer won't change over time
#### 4.4 Output Format
Create an XML file with this structure:
```xml
<evaluation>
<qa_pair>
<question>Find discussions about AI model launches with animal codenames. One model needed a specific safety designation that uses the format ASL-X. What number X was being determined for the model named after a spotted wild cat?</question>
<answer>3</answer>
</qa_pair>
<!-- More qa_pairs... -->
</evaluation>
```
---
# Reference Files
## 📚 Documentation Library
Load these resources as needed during development:
### Core MCP Documentation (Load First)
- **MCP Protocol**: Start with sitemap at `https://modelcontextprotocol.io/sitemap.xml`, then fetch specific pages with `.md` suffix
- [📋 MCP Best Practices](./reference/mcp_best_practices.md) - Universal MCP guidelines including:
- Server and tool naming conventions
- Response format guidelines (JSON vs Markdown)
- Pagination best practices
- Transport selection (streamable HTTP vs stdio)
- Security and error handling standards
### SDK Documentation (Load During Phase 1/2)
- **Python SDK**: Fetch from `https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md`
- **TypeScript SDK**: Fetch from `https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md`
### Language-Specific Implementation Guides (Load During Phase 2)
- [🐍 Python Implementation Guide](./reference/python_mcp_server.md) - Complete Python/FastMCP guide with:
- Server initialization patterns
- Pydantic model examples
- Tool registration with `@mcp.tool`
- Complete working examples
- Quality checklist
- [⚡ TypeScript Implementation Guide](./reference/node_mcp_server.md) - Complete TypeScript guide with:
- Project structure
- Zod schema patterns
- Tool registration with `server.registerTool`
- Complete working examples
- Quality checklist
### Evaluation Guide (Load During Phase 4)
- [✅ Evaluation Guide](./reference/evaluation.md) - Complete evaluation creation guide with:
- Question creation guidelines
- Answer verification strategies
- XML format specifications
- Example questions and answers
- Running an evaluation with the provided scripts
skills/mcp-builder/reference/evaluation.md
+602
View File
@@ -0,0 +1,602 @@
# MCP Server Evaluation Guide
## Overview
This document provides guidance on creating comprehensive evaluations for MCP servers. Evaluations test whether LLMs can effectively use your MCP server to answer realistic, complex questions using only the tools provided.
---
## Quick Reference
### Evaluation Requirements
- Create 10 human-readable questions
- Questions must be READ-ONLY, INDEPENDENT, NON-DESTRUCTIVE
- Each question requires multiple tool calls (potentially dozens)
- Answers must be single, verifiable values
- Answers must be STABLE (won't change over time)
### Output Format
```xml
<evaluation>
<qa_pair>
<question>Your question here</question>
<answer>Single verifiable answer</answer>
</qa_pair>
</evaluation>
```
---
## Purpose of Evaluations
The measure of quality of an MCP server is NOT how well or comprehensively the server implements tools, but how well these implementations (input/output schemas, docstrings/descriptions, functionality) enable LLMs with no other context and access ONLY to the MCP servers to answer realistic and difficult questions.
## Evaluation Overview
Create 10 human-readable questions requiring ONLY READ-ONLY, INDEPENDENT, NON-DESTRUCTIVE, and IDEMPOTENT operations to answer. Each question should be:
- Realistic
- Clear and concise
- Unambiguous
- Complex, requiring potentially dozens of tool calls or steps
- Answerable with a single, verifiable value that you identify in advance
## Question Guidelines
### Core Requirements
1. **Questions MUST be independent**
- Each question should NOT depend on the answer to any other question
- Should not assume prior write operations from processing another question
2. **Questions MUST require ONLY NON-DESTRUCTIVE AND IDEMPOTENT tool use**
- Should not instruct or require modifying state to arrive at the correct answer
3. **Questions must be REALISTIC, CLEAR, CONCISE, and COMPLEX**
- Must require another LLM to use multiple (potentially dozens of) tools or steps to answer
### Complexity and Depth
4. **Questions must require deep exploration**
- Consider multi-hop questions requiring multiple sub-questions and sequential tool calls
- Each step should benefit from information found in previous questions
5. **Questions may require extensive paging**
- May need paging through multiple pages of results
- May require querying old data (1-2 years out-of-date) to find niche information
- The questions must be DIFFICULT
6. **Questions must require deep understanding**
- Rather than surface-level knowledge
- May pose complex ideas as True/False questions requiring evidence
- May use multiple-choice format where LLM must search different hypotheses
7. **Questions must not be solvable with straightforward keyword search**
- Do not include specific keywords from the target content
- Use synonyms, related concepts, or paraphrases
- Require multiple searches, analyzing multiple related items, extracting context, then deriving the answer
### Tool Testing
8. **Questions should stress-test tool return values**
- May elicit tools returning large JSON objects or lists, overwhelming the LLM
- Should require understanding multiple modalities of data:
- IDs and names
- Timestamps and datetimes (months, days, years, seconds)
- File IDs, names, extensions, and mimetypes
- URLs, GIDs, etc.
- Should probe the tool's ability to return all useful forms of data
9. **Questions should MOSTLY reflect real human use cases**
- The kinds of information retrieval tasks that HUMANS assisted by an LLM would care about
10. **Questions may require dozens of tool calls**
- This challenges LLMs with limited context
- Encourages MCP server tools to reduce information returned
11. **Include ambiguous questions**
- May be ambiguous OR require difficult decisions on which tools to call
- Force the LLM to potentially make mistakes or misinterpret
- Ensure that despite AMBIGUITY, there is STILL A SINGLE VERIFIABLE ANSWER
### Stability
12. **Questions must be designed so the answer DOES NOT CHANGE**
- Do not ask questions that rely on "current state" which is dynamic
- For example, do not count:
- Number of reactions to a post
- Number of replies to a thread
- Number of members in a channel
13. **DO NOT let the MCP server RESTRICT the kinds of questions you create**
- Create challenging and complex questions
- Some may not be solvable with the available MCP server tools
- Questions may require specific output formats (datetime vs. epoch time, JSON vs. MARKDOWN)
- Questions may require dozens of tool calls to complete
## Answer Guidelines
### Verification
1. **Answers must be VERIFIABLE via direct string comparison**
- If the answer can be re-written in many formats, clearly specify the output format in the QUESTION
- Examples: "Use YYYY/MM/DD.", "Respond True or False.", "Answer A, B, C, or D and nothing else."
- Answer should be a single VERIFIABLE value such as:
- User ID, user name, display name, first name, last name
- Channel ID, channel name
- Message ID, string
- URL, title
- Numerical quantity
- Timestamp, datetime
- Boolean (for True/False questions)
- Email address, phone number
- File ID, file name, file extension
- Multiple choice answer
- Answers must not require special formatting or complex, structured output
- Answer will be verified using DIRECT STRING COMPARISON
### Readability
2. **Answers should generally prefer HUMAN-READABLE formats**
- Examples: names, first name, last name, datetime, file name, message string, URL, yes/no, true/false, a/b/c/d
- Rather than opaque IDs (though IDs are acceptable)
- The VAST MAJORITY of answers should be human-readable
### Stability
3. **Answers must be STABLE/STATIONARY**
- Look at old content (e.g., conversations that have ended, projects that have launched, questions answered)
- Create QUESTIONS based on "closed" concepts that will always return the same answer
- Questions may ask to consider a fixed time window to insulate from non-stationary answers
- Rely on context UNLIKELY to change
- Example: if finding a paper name, be SPECIFIC enough so answer is not confused with papers published later
4. **Answers must be CLEAR and UNAMBIGUOUS**
- Questions must be designed so there is a single, clear answer
- Answer can be derived from using the MCP server tools
### Diversity
5. **Answers must be DIVERSE**
- Answer should be a single VERIFIABLE value in diverse modalities and formats
- User concept: user ID, user name, display name, first name, last name, email address, phone number
- Channel concept: channel ID, channel name, channel topic
- Message concept: message ID, message string, timestamp, month, day, year
6. **Answers must NOT be complex structures**
- Not a list of values
- Not a complex object
- Not a list of IDs or strings
- Not natural language text
- UNLESS the answer can be straightforwardly verified using DIRECT STRING COMPARISON
- And can be realistically reproduced
- It should be unlikely that an LLM would return the same list in any other order or format
## Evaluation Process
### Step 1: Documentation Inspection
Read the documentation of the target API to understand:
- Available endpoints and functionality
- If ambiguity exists, fetch additional information from the web
- Parallelize this step AS MUCH AS POSSIBLE
- Ensure each subagent is ONLY examining documentation from the file system or on the web
### Step 2: Tool Inspection
List the tools available in the MCP server:
- Inspect the MCP server directly
- Understand input/output schemas, docstrings, and descriptions
- WITHOUT calling the tools themselves at this stage
### Step 3: Developing Understanding
Repeat steps 1 & 2 until you have a good understanding:
- Iterate multiple times
- Think about the kinds of tasks you want to create
- Refine your understanding
- At NO stage should you READ the code of the MCP server implementation itself
- Use your intuition and understanding to create reasonable, realistic, but VERY challenging tasks
### Step 4: Read-Only Content Inspection
After understanding the API and tools, USE the MCP server tools:
- Inspect content using READ-ONLY and NON-DESTRUCTIVE operations ONLY
- Goal: identify specific content (e.g., users, channels, messages, projects, tasks) for creating realistic questions
- Should NOT call any tools that modify state
- Will NOT read the code of the MCP server implementation itself
- Parallelize this step with individual sub-agents pursuing independent explorations
- Ensure each subagent is only performing READ-ONLY, NON-DESTRUCTIVE, and IDEMPOTENT operations
- BE CAREFUL: SOME TOOLS may return LOTS OF DATA which would cause you to run out of CONTEXT
- Make INCREMENTAL, SMALL, AND TARGETED tool calls for exploration
- In all tool call requests, use the `limit` parameter to limit results (<10)
- Use pagination
### Step 5: Task Generation
After inspecting the content, create 10 human-readable questions:
- An LLM should be able to answer these with the MCP server
- Follow all question and answer guidelines above
## Output Format
Each QA pair consists of a question and an answer. The output should be an XML file with this structure:
```xml
<evaluation>
<qa_pair>
<question>Find the project created in Q2 2024 with the highest number of completed tasks. What is the project name?</question>
<answer>Website Redesign</answer>
</qa_pair>
<qa_pair>
<question>Search for issues labeled as "bug" that were closed in March 2024. Which user closed the most issues? Provide their username.</question>
<answer>sarah_dev</answer>
</qa_pair>
<qa_pair>
<question>Look for pull requests that modified files in the /api directory and were merged between January 1 and January 31, 2024. How many different contributors worked on these PRs?</question>
<answer>7</answer>
</qa_pair>
<qa_pair>
<question>Find the repository with the most stars that was created before 2023. What is the repository name?</question>
<answer>data-pipeline</answer>
</qa_pair>
</evaluation>
```
## Evaluation Examples
### Good Questions
**Example 1: Multi-hop question requiring deep exploration (GitHub MCP)**
```xml
<qa_pair>
<question>Find the repository that was archived in Q3 2023 and had previously been the most forked project in the organization. What was the primary programming language used in that repository?</question>
<answer>Python</answer>
</qa_pair>
```
This question is good because:
- Requires multiple searches to find archived repositories
- Needs to identify which had the most forks before archival
- Requires examining repository details for the language
- Answer is a simple, verifiable value
- Based on historical (closed) data that won't change
**Example 2: Requires understanding context without keyword matching (Project Management MCP)**
```xml
<qa_pair>
<question>Locate the initiative focused on improving customer onboarding that was completed in late 2023. The project lead created a retrospective document after completion. What was the lead's role title at that time?</question>
<answer>Product Manager</answer>
</qa_pair>
```
This question is good because:
- Doesn't use specific project name ("initiative focused on improving customer onboarding")
- Requires finding completed projects from specific timeframe
- Needs to identify the project lead and their role
- Requires understanding context from retrospective documents
- Answer is human-readable and stable
- Based on completed work (won't change)
**Example 3: Complex aggregation requiring multiple steps (Issue Tracker MCP)**
```xml
<qa_pair>
<question>Among all bugs reported in January 2024 that were marked as critical priority, which assignee resolved the highest percentage of their assigned bugs within 48 hours? Provide the assignee's username.</question>
<answer>alex_eng</answer>
</qa_pair>
```
This question is good because:
- Requires filtering bugs by date, priority, and status
- Needs to group by assignee and calculate resolution rates
- Requires understanding timestamps to determine 48-hour windows
- Tests pagination (potentially many bugs to process)
- Answer is a single username
- Based on historical data from specific time period
**Example 4: Requires synthesis across multiple data types (CRM MCP)**
```xml
<qa_pair>
<question>Find the account that upgraded from the Starter to Enterprise plan in Q4 2023 and had the highest annual contract value. What industry does this account operate in?</question>
<answer>Healthcare</answer>
</qa_pair>
```
This question is good because:
- Requires understanding subscription tier changes
- Needs to identify upgrade events in specific timeframe
- Requires comparing contract values
- Must access account industry information
- Answer is simple and verifiable
- Based on completed historical transactions
### Poor Questions
**Example 1: Answer changes over time**
```xml
<qa_pair>
<question>How many open issues are currently assigned to the engineering team?</question>
<answer>47</answer>
</qa_pair>
```
This question is poor because:
- The answer will change as issues are created, closed, or reassigned
- Not based on stable/stationary data
- Relies on "current state" which is dynamic
**Example 2: Too easy with keyword search**
```xml
<qa_pair>
<question>Find the pull request with title "Add authentication feature" and tell me who created it.</question>
<answer>developer123</answer>
</qa_pair>
```
This question is poor because:
- Can be solved with a straightforward keyword search for exact title
- Doesn't require deep exploration or understanding
- No synthesis or analysis needed
**Example 3: Ambiguous answer format**
```xml
<qa_pair>
<question>List all the repositories that have Python as their primary language.</question>
<answer>repo1, repo2, repo3, data-pipeline, ml-tools</answer>
</qa_pair>
```
This question is poor because:
- Answer is a list that could be returned in any order
- Difficult to verify with direct string comparison
- LLM might format differently (JSON array, comma-separated, newline-separated)
- Better to ask for a specific aggregate (count) or superlative (most stars)
## Verification Process
After creating evaluations:
1. **Examine the XML file** to understand the schema
2. **Load each task instruction** and in parallel using the MCP server and tools, identify the correct answer by attempting to solve the task YOURSELF
3. **Flag any operations** that require WRITE or DESTRUCTIVE operations
4. **Accumulate all CORRECT answers** and replace any incorrect answers in the document
5. **Remove any `<qa_pair>`** that require WRITE or DESTRUCTIVE operations
Remember to parallelize solving tasks to avoid running out of context, then accumulate all answers and make changes to the file at the end.
## Tips for Creating Quality Evaluations
1. **Think Hard and Plan Ahead** before generating tasks
2. **Parallelize Where Opportunity Arises** to speed up the process and manage context
3. **Focus on Realistic Use Cases** that humans would actually want to accomplish
4. **Create Challenging Questions** that test the limits of the MCP server's capabilities
5. **Ensure Stability** by using historical data and closed concepts
6. **Verify Answers** by solving the questions yourself using the MCP server tools
7. **Iterate and Refine** based on what you learn during the process
---
# Running Evaluations
After creating your evaluation file, you can use the provided evaluation harness to test your MCP server.
## Setup
1. **Install Dependencies**
```bash
pip install -r scripts/requirements.txt
```
Or install manually:
```bash
pip install anthropic mcp
```
2. **Set API Key**
```bash
export ANTHROPIC_API_KEY=your_api_key_here
```
## Evaluation File Format
Evaluation files use XML format with `<qa_pair>` elements:
```xml
<evaluation>
<qa_pair>
<question>Find the project created in Q2 2024 with the highest number of completed tasks. What is the project name?</question>
<answer>Website Redesign</answer>
</qa_pair>
<qa_pair>
<question>Search for issues labeled as "bug" that were closed in March 2024. Which user closed the most issues? Provide their username.</question>
<answer>sarah_dev</answer>
</qa_pair>
</evaluation>
```
## Running Evaluations
The evaluation script (`scripts/evaluation.py`) supports three transport types:
**Important:**
- **stdio transport**: The evaluation script automatically launches and manages the MCP server process for you. Do not run the server manually.
- **sse/http transports**: You must start the MCP server separately before running the evaluation. The script connects to the already-running server at the specified URL.
### 1. Local STDIO Server
For locally-run MCP servers (script launches the server automatically):
```bash
python scripts/evaluation.py \
-t stdio \
-c python \
-a my_mcp_server.py \
evaluation.xml
```
With environment variables:
```bash
python scripts/evaluation.py \
-t stdio \
-c python \
-a my_mcp_server.py \
-e API_KEY=abc123 \
-e DEBUG=true \
evaluation.xml
```
### 2. Server-Sent Events (SSE)
For SSE-based MCP servers (you must start the server first):
```bash
python scripts/evaluation.py \
-t sse \
-u https://example.com/mcp \
-H "Authorization: Bearer token123" \
-H "X-Custom-Header: value" \
evaluation.xml
```
### 3. HTTP (Streamable HTTP)
For HTTP-based MCP servers (you must start the server first):
```bash
python scripts/evaluation.py \
-t http \
-u https://example.com/mcp \
-H "Authorization: Bearer token123" \
evaluation.xml
```
## Command-Line Options
```
usage: evaluation.py [-h] [-t {stdio,sse,http}] [-m MODEL] [-c COMMAND]
[-a ARGS [ARGS ...]] [-e ENV [ENV ...]] [-u URL]
[-H HEADERS [HEADERS ...]] [-o OUTPUT]
eval_file
positional arguments:
eval_file Path to evaluation XML file
optional arguments:
-h, --help Show help message
-t, --transport Transport type: stdio, sse, or http (default: stdio)
-m, --model Claude model to use (default: claude-3-7-sonnet-20250219)
-o, --output Output file for report (default: print to stdout)
stdio options:
-c, --command Command to run MCP server (e.g., python, node)
-a, --args Arguments for the command (e.g., server.py)
-e, --env Environment variables in KEY=VALUE format
sse/http options:
-u, --url MCP server URL
-H, --header HTTP headers in 'Key: Value' format
```
## Output
The evaluation script generates a detailed report including:
- **Summary Statistics**:
- Accuracy (correct/total)
- Average task duration
- Average tool calls per task
- Total tool calls
- **Per-Task Results**:
- Prompt and expected response
- Actual response from the agent
- Whether the answer was correct (✅/❌)
- Duration and tool call details
- Agent's summary of its approach
- Agent's feedback on the tools
### Save Report to File
```bash
python scripts/evaluation.py \
-t stdio \
-c python \
-a my_server.py \
-o evaluation_report.md \
evaluation.xml
```
## Complete Example Workflow
Here's a complete example of creating and running an evaluation:
1. **Create your evaluation file** (`my_evaluation.xml`):
```xml
<evaluation>
<qa_pair>
<question>Find the user who created the most issues in January 2024. What is their username?</question>
<answer>alice_developer</answer>
</qa_pair>
<qa_pair>
<question>Among all pull requests merged in Q1 2024, which repository had the highest number? Provide the repository name.</question>
<answer>backend-api</answer>
</qa_pair>
<qa_pair>
<question>Find the project that was completed in December 2023 and had the longest duration from start to finish. How many days did it take?</question>
<answer>127</answer>
</qa_pair>
</evaluation>
```
2. **Install dependencies**:
```bash
pip install -r scripts/requirements.txt
export ANTHROPIC_API_KEY=your_api_key
```
3. **Run evaluation**:
```bash
python scripts/evaluation.py \
-t stdio \
-c python \
-a github_mcp_server.py \
-e GITHUB_TOKEN=ghp_xxx \
-o github_eval_report.md \
my_evaluation.xml
```
4. **Review the report** in `github_eval_report.md` to:
- See which questions passed/failed
- Read the agent's feedback on your tools
- Identify areas for improvement
- Iterate on your MCP server design
## Troubleshooting
### Connection Errors
If you get connection errors:
- **STDIO**: Verify the command and arguments are correct
- **SSE/HTTP**: Check the URL is accessible and headers are correct
- Ensure any required API keys are set in environment variables or headers
### Low Accuracy
If many evaluations fail:
- Review the agent's feedback for each task
- Check if tool descriptions are clear and comprehensive
- Verify input parameters are well-documented
- Consider whether tools return too much or too little data
- Ensure error messages are actionable
### Timeout Issues
If tasks are timing out:
- Use a more capable model (e.g., `claude-3-7-sonnet-20250219`)
- Check if tools are returning too much data
- Verify pagination is working correctly
- Consider simplifying complex questions
skills/mcp-builder/reference/mcp_best_practices.md
+249
View File
@@ -0,0 +1,249 @@
# MCP Server Best Practices
## Quick Reference
### Server Naming
- **Python**: `{service}_mcp` (e.g., `slack_mcp`)
- **Node/TypeScript**: `{service}-mcp-server` (e.g., `slack-mcp-server`)
### Tool Naming
- Use snake_case with service prefix
- Format: `{service}_{action}_{resource}`
- Example: `slack_send_message`, `github_create_issue`
### Response Formats
- Support both JSON and Markdown formats
- JSON for programmatic processing
- Markdown for human readability
### Pagination
- Always respect `limit` parameter
- Return `has_more`, `next_offset`, `total_count`
- Default to 20-50 items
### Transport
- **Streamable HTTP**: For remote servers, multi-client scenarios
- **stdio**: For local integrations, command-line tools
- Avoid SSE (deprecated in favor of streamable HTTP)
---
## Server Naming Conventions
Follow these standardized naming patterns:
**Python**: Use format `{service}_mcp` (lowercase with underscores)
- Examples: `slack_mcp`, `github_mcp`, `jira_mcp`
**Node/TypeScript**: Use format `{service}-mcp-server` (lowercase with hyphens)
- Examples: `slack-mcp-server`, `github-mcp-server`, `jira-mcp-server`
The name should be general, descriptive of the service being integrated, easy to infer from the task description, and without version numbers.
---
## Tool Naming and Design
### Tool Naming
1. **Use snake_case**: `search_users`, `create_project`, `get_channel_info`
2. **Include service prefix**: Anticipate that your MCP server may be used alongside other MCP servers
- Use `slack_send_message` instead of just `send_message`
- Use `github_create_issue` instead of just `create_issue`
3. **Be action-oriented**: Start with verbs (get, list, search, create, etc.)
4. **Be specific**: Avoid generic names that could conflict with other servers
### Tool Design
- Tool descriptions must narrowly and unambiguously describe functionality
- Descriptions must precisely match actual functionality
- Provide tool annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint)
- Keep tool operations focused and atomic
---
## Response Formats
All tools that return data should support multiple formats:
### JSON Format (`response_format="json"`)
- Machine-readable structured data
- Include all available fields and metadata
- Consistent field names and types
- Use for programmatic processing
### Markdown Format (`response_format="markdown"`, typically default)
- Human-readable formatted text
- Use headers, lists, and formatting for clarity
- Convert timestamps to human-readable format
- Show display names with IDs in parentheses
- Omit verbose metadata
---
## Pagination
For tools that list resources:
- **Always respect the `limit` parameter**
- **Implement pagination**: Use `offset` or cursor-based pagination
- **Return pagination metadata**: Include `has_more`, `next_offset`/`next_cursor`, `total_count`
- **Never load all results into memory**: Especially important for large datasets
- **Default to reasonable limits**: 20-50 items is typical
Example pagination response:
```json
{
"total": 150,
"count": 20,
"offset": 0,
"items": [...],
"has_more": true,
"next_offset": 20
}
```
---
## Transport Options
### Streamable HTTP
**Best for**: Remote servers, web services, multi-client scenarios
**Characteristics**:
- Bidirectional communication over HTTP
- Supports multiple simultaneous clients
- Can be deployed as a web service
- Enables server-to-client notifications
**Use when**:
- Serving multiple clients simultaneously
- Deploying as a cloud service
- Integration with web applications
### stdio
**Best for**: Local integrations, command-line tools
**Characteristics**:
- Standard input/output stream communication
- Simple setup, no network configuration needed
- Runs as a subprocess of the client
**Use when**:
- Building tools for local development environments
- Integrating with desktop applications
- Single-user, single-session scenarios
**Note**: stdio servers should NOT log to stdout (use stderr for logging)
### Transport Selection
| Criterion | stdio | Streamable HTTP |
|-----------|-------|-----------------|
| **Deployment** | Local | Remote |
| **Clients** | Single | Multiple |
| **Complexity** | Low | Medium |
| **Real-time** | No | Yes |
---
## Security Best Practices
### Authentication and Authorization
**OAuth 2.1**:
- Use secure OAuth 2.1 with certificates from recognized authorities
- Validate access tokens before processing requests
- Only accept tokens specifically intended for your server
**API Keys**:
- Store API keys in environment variables, never in code
- Validate keys on server startup
- Provide clear error messages when authentication fails
### Input Validation
- Sanitize file paths to prevent directory traversal
- Validate URLs and external identifiers
- Check parameter sizes and ranges
- Prevent command injection in system calls
- Use schema validation (Pydantic/Zod) for all inputs
### Error Handling
- Don't expose internal errors to clients
- Log security-relevant errors server-side
- Provide helpful but not revealing error messages
- Clean up resources after errors
### DNS Rebinding Protection
For streamable HTTP servers running locally:
- Enable DNS rebinding protection
- Validate the `Origin` header on all incoming connections
- Bind to `127.0.0.1` rather than `0.0.0.0`
---
## Tool Annotations
Provide annotations to help clients understand tool behavior:
| Annotation | Type | Default | Description |
|-----------|------|---------|-------------|
| `readOnlyHint` | boolean | false | Tool does not modify its environment |
| `destructiveHint` | boolean | true | Tool may perform destructive updates |
| `idempotentHint` | boolean | false | Repeated calls with same args have no additional effect |
| `openWorldHint` | boolean | true | Tool interacts with external entities |
**Important**: Annotations are hints, not security guarantees. Clients should not make security-critical decisions based solely on annotations.
---
## Error Handling
- Use standard JSON-RPC error codes
- Report tool errors within result objects (not protocol-level errors)
- Provide helpful, specific error messages with suggested next steps
- Don't expose internal implementation details
- Clean up resources properly on errors
Example error handling:
```typescript
try {
const result = performOperation();
return { content: [{ type: "text", text: result }] };
} catch (error) {
return {
isError: true,
content: [{
type: "text",
text: `Error: ${error.message}. Try using filter='active_only' to reduce results.`
}]
};
}
```
---
## Testing Requirements
Comprehensive testing should cover:
- **Functional testing**: Verify correct execution with valid/invalid inputs
- **Integration testing**: Test interaction with external systems
- **Security testing**: Validate auth, input sanitization, rate limiting
- **Performance testing**: Check behavior under load, timeouts
- **Error handling**: Ensure proper error reporting and cleanup
---
## Documentation Requirements
- Provide clear documentation of all tools and capabilities
- Include working examples (at least 3 per major feature)
- Document security considerations
- Specify required permissions and access levels
- Document rate limits and performance characteristics
skills/mcp-builder/reference/node_mcp_server.md
+970
View File
@@ -0,0 +1,970 @@
# Node/TypeScript MCP Server Implementation Guide
## Overview
This document provides Node/TypeScript-specific best practices and examples for implementing MCP servers using the MCP TypeScript SDK. It covers project structure, server setup, tool registration patterns, input validation with Zod, error handling, and complete working examples.
---
## Quick Reference
### Key Imports
```typescript
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StreamableHTTPServerTransport } from "@modelcontextprotocol/sdk/server/streamableHttp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import express from "express";
import { z } from "zod";
```
### Server Initialization
```typescript
const server = new McpServer({
name: "service-mcp-server",
version: "1.0.0"
});
```
### Tool Registration Pattern
```typescript
server.registerTool(
"tool_name",
{
title: "Tool Display Name",
description: "What the tool does",
inputSchema: { param: z.string() },
outputSchema: { result: z.string() }
},
async ({ param }) => {
const output = { result: `Processed: ${param}` };
return {
content: [{ type: "text", text: JSON.stringify(output) }],
structuredContent: output // Modern pattern for structured data
};
}
);
```
---
## MCP TypeScript SDK
The official MCP TypeScript SDK provides:
- `McpServer` class for server initialization
- `registerTool` method for tool registration
- Zod schema integration for runtime input validation
- Type-safe tool handler implementations
**IMPORTANT - Use Modern APIs Only:**
- **DO use**: `server.registerTool()`, `server.registerResource()`, `server.registerPrompt()`
- **DO NOT use**: Old deprecated APIs such as `server.tool()`, `server.setRequestHandler(ListToolsRequestSchema, ...)`, or manual handler registration
- The `register*` methods provide better type safety, automatic schema handling, and are the recommended approach
See the MCP SDK documentation in the references for complete details.
## Server Naming Convention
Node/TypeScript MCP servers must follow this naming pattern:
- **Format**: `{service}-mcp-server` (lowercase with hyphens)
- **Examples**: `github-mcp-server`, `jira-mcp-server`, `stripe-mcp-server`
The name should be:
- General (not tied to specific features)
- Descriptive of the service/API being integrated
- Easy to infer from the task description
- Without version numbers or dates
## Project Structure
Create the following structure for Node/TypeScript MCP servers:
```
{service}-mcp-server/
├── package.json
├── tsconfig.json
├── README.md
├── src/
│ ├── index.ts # Main entry point with McpServer initialization
│ ├── types.ts # TypeScript type definitions and interfaces
│ ├── tools/ # Tool implementations (one file per domain)
│ ├── services/ # API clients and shared utilities
│ ├── schemas/ # Zod validation schemas
│ └── constants.ts # Shared constants (API_URL, CHARACTER_LIMIT, etc.)
└── dist/ # Built JavaScript files (entry point: dist/index.js)
```
## Tool Implementation
### Tool Naming
Use snake_case for tool names (e.g., "search_users", "create_project", "get_channel_info") with clear, action-oriented names.
**Avoid Naming Conflicts**: Include the service context to prevent overlaps:
- Use "slack_send_message" instead of just "send_message"
- Use "github_create_issue" instead of just "create_issue"
- Use "asana_list_tasks" instead of just "list_tasks"
### Tool Structure
Tools are registered using the `registerTool` method with the following requirements:
- Use Zod schemas for runtime input validation and type safety
- The `description` field must be explicitly provided - JSDoc comments are NOT automatically extracted
- Explicitly provide `title`, `description`, `inputSchema`, and `annotations`
- The `inputSchema` must be a Zod schema object (not a JSON schema)
- Type all parameters and return values explicitly
```typescript
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { z } from "zod";
const server = new McpServer({
name: "example-mcp",
version: "1.0.0"
});
// Zod schema for input validation
const UserSearchInputSchema = z.object({
query: z.string()
.min(2, "Query must be at least 2 characters")
.max(200, "Query must not exceed 200 characters")
.describe("Search string to match against names/emails"),
limit: z.number()
.int()
.min(1)
.max(100)
.default(20)
.describe("Maximum results to return"),
offset: z.number()
.int()
.min(0)
.default(0)
.describe("Number of results to skip for pagination"),
response_format: z.nativeEnum(ResponseFormat)
.default(ResponseFormat.MARKDOWN)
.describe("Output format: 'markdown' for human-readable or 'json' for machine-readable")
}).strict();
// Type definition from Zod schema
type UserSearchInput = z.infer<typeof UserSearchInputSchema>;
server.registerTool(
"example_search_users",
{
title: "Search Example Users",
description: `Search for users in the Example system by name, email, or team.
This tool searches across all user profiles in the Example platform, supporting partial matches and various search filters. It does NOT create or modify users, only searches existing ones.
Args:
- query (string): Search string to match against names/emails
- limit (number): Maximum results to return, between 1-100 (default: 20)
- offset (number): Number of results to skip for pagination (default: 0)
- response_format ('markdown' | 'json'): Output format (default: 'markdown')
Returns:
For JSON format: Structured data with schema:
{
"total": number, // Total number of matches found
"count": number, // Number of results in this response
"offset": number, // Current pagination offset
"users": [
{
"id": string, // User ID (e.g., "U123456789")
"name": string, // Full name (e.g., "John Doe")
"email": string, // Email address
"team": string, // Team name (optional)
"active": boolean // Whether user is active
}
],
"has_more": boolean, // Whether more results are available
"next_offset": number // Offset for next page (if has_more is true)
}
Examples:
- Use when: "Find all marketing team members" -> params with query="team:marketing"
- Use when: "Search for John's account" -> params with query="john"
- Don't use when: You need to create a user (use example_create_user instead)
Error Handling:
- Returns "Error: Rate limit exceeded" if too many requests (429 status)
- Returns "No users found matching '<query>'" if search returns empty`,
inputSchema: UserSearchInputSchema,
annotations: {
readOnlyHint: true,
destructiveHint: false,
idempotentHint: true,
openWorldHint: true
}
},
async (params: UserSearchInput) => {
try {
// Input validation is handled by Zod schema
// Make API request using validated parameters
const data = await makeApiRequest<any>(
"users/search",
"GET",
undefined,
{
q: params.query,
limit: params.limit,
offset: params.offset
}
);
const users = data.users || [];
const total = data.total || 0;
if (!users.length) {
return {
content: [{
type: "text",
text: `No users found matching '${params.query}'`
}]
};
}
// Prepare structured output
const output = {
total,
count: users.length,
offset: params.offset,
users: users.map((user: any) => ({
id: user.id,
name: user.name,
email: user.email,
...(user.team ? { team: user.team } : {}),
active: user.active ?? true
})),
has_more: total > params.offset + users.length,
...(total > params.offset + users.length ? {
next_offset: params.offset + users.length
} : {})
};
// Format text representation based on requested format
let textContent: string;
if (params.response_format === ResponseFormat.MARKDOWN) {
const lines = [`# User Search Results: '${params.query}'`, "",
`Found ${total} users (showing ${users.length})`, ""];
for (const user of users) {
lines.push(`## ${user.name} (${user.id})`);
lines.push(`- **Email**: ${user.email}`);
if (user.team) lines.push(`- **Team**: ${user.team}`);
lines.push("");
}
textContent = lines.join("\n");
} else {
textContent = JSON.stringify(output, null, 2);
}
return {
content: [{ type: "text", text: textContent }],
structuredContent: output // Modern pattern for structured data
};
} catch (error) {
return {
content: [{
type: "text",
text: handleApiError(error)
}]
};
}
}
);
```
## Zod Schemas for Input Validation
Zod provides runtime type validation:
```typescript
import { z } from "zod";
// Basic schema with validation
const CreateUserSchema = z.object({
name: z.string()
.min(1, "Name is required")
.max(100, "Name must not exceed 100 characters"),
email: z.string()
.email("Invalid email format"),
age: z.number()
.int("Age must be a whole number")
.min(0, "Age cannot be negative")
.max(150, "Age cannot be greater than 150")
}).strict(); // Use .strict() to forbid extra fields
// Enums
enum ResponseFormat {
MARKDOWN = "markdown",
JSON = "json"
}
const SearchSchema = z.object({
response_format: z.nativeEnum(ResponseFormat)
.default(ResponseFormat.MARKDOWN)
.describe("Output format")
});
// Optional fields with defaults
const PaginationSchema = z.object({
limit: z.number()
.int()
.min(1)
.max(100)
.default(20)
.describe("Maximum results to return"),
offset: z.number()
.int()
.min(0)
.default(0)
.describe("Number of results to skip")
});
```
## Response Format Options
Support multiple output formats for flexibility:
```typescript
enum ResponseFormat {
MARKDOWN = "markdown",
JSON = "json"
}
const inputSchema = z.object({
query: z.string(),
response_format: z.nativeEnum(ResponseFormat)
.default(ResponseFormat.MARKDOWN)
.describe("Output format: 'markdown' for human-readable or 'json' for machine-readable")
});
```
**Markdown format**:
- Use headers, lists, and formatting for clarity
- Convert timestamps to human-readable format
- Show display names with IDs in parentheses
- Omit verbose metadata
- Group related information logically
**JSON format**:
- Return complete, structured data suitable for programmatic processing
- Include all available fields and metadata
- Use consistent field names and types
## Pagination Implementation
For tools that list resources:
```typescript
const ListSchema = z.object({
limit: z.number().int().min(1).max(100).default(20),
offset: z.number().int().min(0).default(0)
});
async function listItems(params: z.infer<typeof ListSchema>) {
const data = await apiRequest(params.limit, params.offset);
const response = {
total: data.total,
count: data.items.length,
offset: params.offset,
items: data.items,
has_more: data.total > params.offset + data.items.length,
next_offset: data.total > params.offset + data.items.length
? params.offset + data.items.length
: undefined
};
return JSON.stringify(response, null, 2);
}
```
## Character Limits and Truncation
Add a CHARACTER_LIMIT constant to prevent overwhelming responses:
```typescript
// At module level in constants.ts
export const CHARACTER_LIMIT = 25000; // Maximum response size in characters
async function searchTool(params: SearchInput) {
let result = generateResponse(data);
// Check character limit and truncate if needed
if (result.length > CHARACTER_LIMIT) {
const truncatedData = data.slice(0, Math.max(1, data.length / 2));
response.data = truncatedData;
response.truncated = true;
response.truncation_message =
`Response truncated from ${data.length} to ${truncatedData.length} items. ` +
`Use 'offset' parameter or add filters to see more results.`;
result = JSON.stringify(response, null, 2);
}
return result;
}
```
## Error Handling
Provide clear, actionable error messages:
```typescript
import axios, { AxiosError } from "axios";
function handleApiError(error: unknown): string {
if (error instanceof AxiosError) {
if (error.response) {
switch (error.response.status) {
case 404:
return "Error: Resource not found. Please check the ID is correct.";
case 403:
return "Error: Permission denied. You don't have access to this resource.";
case 429:
return "Error: Rate limit exceeded. Please wait before making more requests.";
default:
return `Error: API request failed with status ${error.response.status}`;
}
} else if (error.code === "ECONNABORTED") {
return "Error: Request timed out. Please try again.";
}
}
return `Error: Unexpected error occurred: ${error instanceof Error ? error.message : String(error)}`;
}
```
## Shared Utilities
Extract common functionality into reusable functions:
```typescript
// Shared API request function
async function makeApiRequest<T>(
endpoint: string,
method: "GET" | "POST" | "PUT" | "DELETE" = "GET",
data?: any,
params?: any
): Promise<T> {
try {
const response = await axios({
method,
url: `${API_BASE_URL}/${endpoint}`,
data,
params,
timeout: 30000,
headers: {
"Content-Type": "application/json",
"Accept": "application/json"
}
});
return response.data;
} catch (error) {
throw error;
}
}
```
## Async/Await Best Practices
Always use async/await for network requests and I/O operations:
```typescript
// Good: Async network request
async function fetchData(resourceId: string): Promise<ResourceData> {
const response = await axios.get(`${API_URL}/resource/${resourceId}`);
return response.data;
}
// Bad: Promise chains
function fetchData(resourceId: string): Promise<ResourceData> {
return axios.get(`${API_URL}/resource/${resourceId}`)
.then(response => response.data); // Harder to read and maintain
}
```
## TypeScript Best Practices
1. **Use Strict TypeScript**: Enable strict mode in tsconfig.json
2. **Define Interfaces**: Create clear interface definitions for all data structures
3. **Avoid `any`**: Use proper types or `unknown` instead of `any`
4. **Zod for Runtime Validation**: Use Zod schemas to validate external data
5. **Type Guards**: Create type guard functions for complex type checking
6. **Error Handling**: Always use try-catch with proper error type checking
7. **Null Safety**: Use optional chaining (`?.`) and nullish coalescing (`??`)
```typescript
// Good: Type-safe with Zod and interfaces
interface UserResponse {
id: string;
name: string;
email: string;
team?: string;
active: boolean;
}
const UserSchema = z.object({
id: z.string(),
name: z.string(),
email: z.string().email(),
team: z.string().optional(),
active: z.boolean()
});
type User = z.infer<typeof UserSchema>;
async function getUser(id: string): Promise<User> {
const data = await apiCall(`/users/${id}`);
return UserSchema.parse(data); // Runtime validation
}
// Bad: Using any
async function getUser(id: string): Promise<any> {
return await apiCall(`/users/${id}`); // No type safety
}
```
## Package Configuration
### package.json
```json
{
"name": "{service}-mcp-server",
"version": "1.0.0",
"description": "MCP server for {Service} API integration",
"type": "module",
"main": "dist/index.js",
"scripts": {
"start": "node dist/index.js",
"dev": "tsx watch src/index.ts",
"build": "tsc",
"clean": "rm -rf dist"
},
"engines": {
"node": ">=18"
},
"dependencies": {
"@modelcontextprotocol/sdk": "^1.6.1",
"axios": "^1.7.9",
"zod": "^3.23.8"
},
"devDependencies": {
"@types/node": "^22.10.0",
"tsx": "^4.19.2",
"typescript": "^5.7.2"
}
}
```
### tsconfig.json
```json
{
"compilerOptions": {
"target": "ES2022",
"module": "Node16",
"moduleResolution": "Node16",
"lib": ["ES2022"],
"outDir": "./dist",
"rootDir": "./src",
"strict": true,
"esModuleInterop": true,
"skipLibCheck": true,
"forceConsistentCasingInFileNames": true,
"declaration": true,
"declarationMap": true,
"sourceMap": true,
"allowSyntheticDefaultImports": true
},
"include": ["src/**/*"],
"exclude": ["node_modules", "dist"]
}
```
## Complete Example
```typescript
#!/usr/bin/env node
/**
* MCP Server for Example Service.
*
* This server provides tools to interact with Example API, including user search,
* project management, and data export capabilities.
*/
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
import axios, { AxiosError } from "axios";
// Constants
const API_BASE_URL = "https://api.example.com/v1";
const CHARACTER_LIMIT = 25000;
// Enums
enum ResponseFormat {
MARKDOWN = "markdown",
JSON = "json"
}
// Zod schemas
const UserSearchInputSchema = z.object({
query: z.string()
.min(2, "Query must be at least 2 characters")
.max(200, "Query must not exceed 200 characters")
.describe("Search string to match against names/emails"),
limit: z.number()
.int()
.min(1)
.max(100)
.default(20)
.describe("Maximum results to return"),
offset: z.number()
.int()
.min(0)
.default(0)
.describe("Number of results to skip for pagination"),
response_format: z.nativeEnum(ResponseFormat)
.default(ResponseFormat.MARKDOWN)
.describe("Output format: 'markdown' for human-readable or 'json' for machine-readable")
}).strict();
type UserSearchInput = z.infer<typeof UserSearchInputSchema>;
// Shared utility functions
async function makeApiRequest<T>(
endpoint: string,
method: "GET" | "POST" | "PUT" | "DELETE" = "GET",
data?: any,
params?: any
): Promise<T> {
try {
const response = await axios({
method,
url: `${API_BASE_URL}/${endpoint}`,
data,
params,
timeout: 30000,
headers: {
"Content-Type": "application/json",
"Accept": "application/json"
}
});
return response.data;
} catch (error) {
throw error;
}
}
function handleApiError(error: unknown): string {
if (error instanceof AxiosError) {
if (error.response) {
switch (error.response.status) {
case 404:
return "Error: Resource not found. Please check the ID is correct.";
case 403:
return "Error: Permission denied. You don't have access to this resource.";
case 429:
return "Error: Rate limit exceeded. Please wait before making more requests.";
default:
return `Error: API request failed with status ${error.response.status}`;
}
} else if (error.code === "ECONNABORTED") {
return "Error: Request timed out. Please try again.";
}
}
return `Error: Unexpected error occurred: ${error instanceof Error ? error.message : String(error)}`;
}
// Create MCP server instance
const server = new McpServer({
name: "example-mcp",
version: "1.0.0"
});
// Register tools
server.registerTool(
"example_search_users",
{
title: "Search Example Users",
description: `[Full description as shown above]`,
inputSchema: UserSearchInputSchema,
annotations: {
readOnlyHint: true,
destructiveHint: false,
idempotentHint: true,
openWorldHint: true
}
},
async (params: UserSearchInput) => {
// Implementation as shown above
}
);
// Main function
// For stdio (local):
async function runStdio() {
if (!process.env.EXAMPLE_API_KEY) {
console.error("ERROR: EXAMPLE_API_KEY environment variable is required");
process.exit(1);
}
const transport = new StdioServerTransport();
await server.connect(transport);
console.error("MCP server running via stdio");
}
// For streamable HTTP (remote):
async function runHTTP() {
if (!process.env.EXAMPLE_API_KEY) {
console.error("ERROR: EXAMPLE_API_KEY environment variable is required");
process.exit(1);
}
const app = express();
app.use(express.json());
app.post('/mcp', async (req, res) => {
const transport = new StreamableHTTPServerTransport({
sessionIdGenerator: undefined,
enableJsonResponse: true
});
res.on('close', () => transport.close());
await server.connect(transport);
await transport.handleRequest(req, res, req.body);
});
const port = parseInt(process.env.PORT || '3000');
app.listen(port, () => {
console.error(`MCP server running on http://localhost:${port}/mcp`);
});
}
// Choose transport based on environment
const transport = process.env.TRANSPORT || 'stdio';
if (transport === 'http') {
runHTTP().catch(error => {
console.error("Server error:", error);
process.exit(1);
});
} else {
runStdio().catch(error => {
console.error("Server error:", error);
process.exit(1);
});
}
```
---
## Advanced MCP Features
### Resource Registration
Expose data as resources for efficient, URI-based access:
```typescript
import { ResourceTemplate } from "@modelcontextprotocol/sdk/types.js";
// Register a resource with URI template
server.registerResource(
{
uri: "file://documents/{name}",
name: "Document Resource",
description: "Access documents by name",
mimeType: "text/plain"
},
async (uri: string) => {
// Extract parameter from URI
const match = uri.match(/^file:\/\/documents\/(.+)$/);
if (!match) {
throw new Error("Invalid URI format");
}
const documentName = match[1];
const content = await loadDocument(documentName);
return {
contents: [{
uri,
mimeType: "text/plain",
text: content
}]
};
}
);
// List available resources dynamically
server.registerResourceList(async () => {
const documents = await getAvailableDocuments();
return {
resources: documents.map(doc => ({
uri: `file://documents/${doc.name}`,
name: doc.name,
mimeType: "text/plain",
description: doc.description
}))
};
});
```
**When to use Resources vs Tools:**
- **Resources**: For data access with simple URI-based parameters
- **Tools**: For complex operations requiring validation and business logic
- **Resources**: When data is relatively static or template-based
- **Tools**: When operations have side effects or complex workflows
### Transport Options
The TypeScript SDK supports two main transport mechanisms:
#### Streamable HTTP (Recommended for Remote Servers)
```typescript
import { StreamableHTTPServerTransport } from "@modelcontextprotocol/sdk/server/streamableHttp.js";
import express from "express";
const app = express();
app.use(express.json());
app.post('/mcp', async (req, res) => {
// Create new transport for each request (stateless, prevents request ID collisions)
const transport = new StreamableHTTPServerTransport({
sessionIdGenerator: undefined,
enableJsonResponse: true
});
res.on('close', () => transport.close());
await server.connect(transport);
await transport.handleRequest(req, res, req.body);
});
app.listen(3000);
```
#### stdio (For Local Integrations)
```typescript
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
const transport = new StdioServerTransport();
await server.connect(transport);
```
**Transport selection:**
- **Streamable HTTP**: Web services, remote access, multiple clients
- **stdio**: Command-line tools, local development, subprocess integration
### Notification Support
Notify clients when server state changes:
```typescript
// Notify when tools list changes
server.notification({
method: "notifications/tools/list_changed"
});
// Notify when resources change
server.notification({
method: "notifications/resources/list_changed"
});
```
Use notifications sparingly - only when server capabilities genuinely change.
---
## Code Best Practices
### Code Composability and Reusability
Your implementation MUST prioritize composability and code reuse:
1. **Extract Common Functionality**:
- Create reusable helper functions for operations used across multiple tools
- Build shared API clients for HTTP requests instead of duplicating code
- Centralize error handling logic in utility functions
- Extract business logic into dedicated functions that can be composed
- Extract shared markdown or JSON field selection & formatting functionality
2. **Avoid Duplication**:
- NEVER copy-paste similar code between tools
- If you find yourself writing similar logic twice, extract it into a function
- Common operations like pagination, filtering, field selection, and formatting should be shared
- Authentication/authorization logic should be centralized
## Building and Running
Always build your TypeScript code before running:
```bash
# Build the project
npm run build
# Run the server
npm start
# Development with auto-reload
npm run dev
```
Always ensure `npm run build` completes successfully before considering the implementation complete.
## Quality Checklist
Before finalizing your Node/TypeScript MCP server implementation, ensure:
### Strategic Design
- [ ] Tools enable complete workflows, not just API endpoint wrappers
- [ ] Tool names reflect natural task subdivisions
- [ ] Response formats optimize for agent context efficiency
- [ ] Human-readable identifiers used where appropriate
- [ ] Error messages guide agents toward correct usage
### Implementation Quality
- [ ] FOCUSED IMPLEMENTATION: Most important and valuable tools implemented
- [ ] All tools registered using `registerTool` with complete configuration
- [ ] All tools include `title`, `description`, `inputSchema`, and `annotations`
- [ ] Annotations correctly set (readOnlyHint, destructiveHint, idempotentHint, openWorldHint)
- [ ] All tools use Zod schemas for runtime input validation with `.strict()` enforcement
- [ ] All Zod schemas have proper constraints and descriptive error messages
- [ ] All tools have comprehensive descriptions with explicit input/output types
- [ ] Descriptions include return value examples and complete schema documentation
- [ ] Error messages are clear, actionable, and educational
### TypeScript Quality
- [ ] TypeScript interfaces are defined for all data structures
- [ ] Strict TypeScript is enabled in tsconfig.json
- [ ] No use of `any` type - use `unknown` or proper types instead
- [ ] All async functions have explicit Promise<T> return types
- [ ] Error handling uses proper type guards (e.g., `axios.isAxiosError`, `z.ZodError`)
### Advanced Features (where applicable)
- [ ] Resources registered for appropriate data endpoints
- [ ] Appropriate transport configured (stdio or streamable HTTP)
- [ ] Notifications implemented for dynamic server capabilities
- [ ] Type-safe with SDK interfaces
### Project Configuration
- [ ] Package.json includes all necessary dependencies
- [ ] Build script produces working JavaScript in dist/ directory
- [ ] Main entry point is properly configured as dist/index.js
- [ ] Server name follows format: `{service}-mcp-server`
- [ ] tsconfig.json properly configured with strict mode
### Code Quality
- [ ] Pagination is properly implemented where applicable
- [ ] Large responses check CHARACTER_LIMIT constant and truncate with clear messages
- [ ] Filtering options are provided for potentially large result sets
- [ ] All network operations handle timeouts and connection errors gracefully
- [ ] Common functionality is extracted into reusable functions
- [ ] Return types are consistent across similar operations
### Testing and Build
- [ ] `npm run build` completes successfully without errors
- [ ] dist/index.js created and executable
- [ ] Server runs: `node dist/index.js --help`
- [ ] All imports resolve correctly
- [ ] Sample tool calls work as expected
skills/mcp-builder/reference/python_mcp_server.md
+719
View File
@@ -0,0 +1,719 @@
# Python MCP Server Implementation Guide
## Overview
This document provides Python-specific best practices and examples for implementing MCP servers using the MCP Python SDK. It covers server setup, tool registration patterns, input validation with Pydantic, error handling, and complete working examples.
---
## Quick Reference
### Key Imports
```python
from mcp.server.fastmcp import FastMCP
from pydantic import BaseModel, Field, field_validator, ConfigDict
from typing import Optional, List, Dict, Any
from enum import Enum
import httpx
```
### Server Initialization
```python
mcp = FastMCP("service_mcp")
```
### Tool Registration Pattern
```python
@mcp.tool(name="tool_name", annotations={...})
async def tool_function(params: InputModel) -> str:
# Implementation
pass
```
---
## MCP Python SDK and FastMCP
The official MCP Python SDK provides FastMCP, a high-level framework for building MCP servers. It provides:
- Automatic description and inputSchema generation from function signatures and docstrings
- Pydantic model integration for input validation
- Decorator-based tool registration with `@mcp.tool`
**For complete SDK documentation, use WebFetch to load:**
`https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md`
## Server Naming Convention
Python MCP servers must follow this naming pattern:
- **Format**: `{service}_mcp` (lowercase with underscores)
- **Examples**: `github_mcp`, `jira_mcp`, `stripe_mcp`
The name should be:
- General (not tied to specific features)
- Descriptive of the service/API being integrated
- Easy to infer from the task description
- Without version numbers or dates
## Tool Implementation
### Tool Naming
Use snake_case for tool names (e.g., "search_users", "create_project", "get_channel_info") with clear, action-oriented names.
**Avoid Naming Conflicts**: Include the service context to prevent overlaps:
- Use "slack_send_message" instead of just "send_message"
- Use "github_create_issue" instead of just "create_issue"
- Use "asana_list_tasks" instead of just "list_tasks"
### Tool Structure with FastMCP
Tools are defined using the `@mcp.tool` decorator with Pydantic models for input validation:
```python
from pydantic import BaseModel, Field, ConfigDict
from mcp.server.fastmcp import FastMCP
# Initialize the MCP server
mcp = FastMCP("example_mcp")
# Define Pydantic model for input validation
class ServiceToolInput(BaseModel):
'''Input model for service tool operation.'''
model_config = ConfigDict(
str_strip_whitespace=True, # Auto-strip whitespace from strings
validate_assignment=True, # Validate on assignment
extra='forbid' # Forbid extra fields
)
param1: str = Field(..., description="First parameter description (e.g., 'user123', 'project-abc')", min_length=1, max_length=100)
param2: Optional[int] = Field(default=None, description="Optional integer parameter with constraints", ge=0, le=1000)
tags: Optional[List[str]] = Field(default_factory=list, description="List of tags to apply", max_items=10)
@mcp.tool(
name="service_tool_name",
annotations={
"title": "Human-Readable Tool Title",
"readOnlyHint": True, # Tool does not modify environment
"destructiveHint": False, # Tool does not perform destructive operations
"idempotentHint": True, # Repeated calls have no additional effect
"openWorldHint": False # Tool does not interact with external entities
}
)
async def service_tool_name(params: ServiceToolInput) -> str:
'''Tool description automatically becomes the 'description' field.
This tool performs a specific operation on the service. It validates all inputs
using the ServiceToolInput Pydantic model before processing.
Args:
params (ServiceToolInput): Validated input parameters containing:
- param1 (str): First parameter description
- param2 (Optional[int]): Optional parameter with default
- tags (Optional[List[str]]): List of tags
Returns:
str: JSON-formatted response containing operation results
'''
# Implementation here
pass
```
## Pydantic v2 Key Features
- Use `model_config` instead of nested `Config` class
- Use `field_validator` instead of deprecated `validator`
- Use `model_dump()` instead of deprecated `dict()`
- Validators require `@classmethod` decorator
- Type hints are required for validator methods
```python
from pydantic import BaseModel, Field, field_validator, ConfigDict
class CreateUserInput(BaseModel):
model_config = ConfigDict(
str_strip_whitespace=True,
validate_assignment=True
)
name: str = Field(..., description="User's full name", min_length=1, max_length=100)
email: str = Field(..., description="User's email address", pattern=r'^[\w\.-]+@[\w\.-]+\.\w+$')
age: int = Field(..., description="User's age", ge=0, le=150)
@field_validator('email')
@classmethod
def validate_email(cls, v: str) -> str:
if not v.strip():
raise ValueError("Email cannot be empty")
return v.lower()
```
## Response Format Options
Support multiple output formats for flexibility:
```python
from enum import Enum
class ResponseFormat(str, Enum):
'''Output format for tool responses.'''
MARKDOWN = "markdown"
JSON = "json"
class UserSearchInput(BaseModel):
query: str = Field(..., description="Search query")
response_format: ResponseFormat = Field(
default=ResponseFormat.MARKDOWN,
description="Output format: 'markdown' for human-readable or 'json' for machine-readable"
)
```
**Markdown format**:
- Use headers, lists, and formatting for clarity
- Convert timestamps to human-readable format (e.g., "2024-01-15 10:30:00 UTC" instead of epoch)
- Show display names with IDs in parentheses (e.g., "@john.doe (U123456)")
- Omit verbose metadata (e.g., show only one profile image URL, not all sizes)
- Group related information logically
**JSON format**:
- Return complete, structured data suitable for programmatic processing
- Include all available fields and metadata
- Use consistent field names and types
## Pagination Implementation
For tools that list resources:
```python
class ListInput(BaseModel):
limit: Optional[int] = Field(default=20, description="Maximum results to return", ge=1, le=100)
offset: Optional[int] = Field(default=0, description="Number of results to skip for pagination", ge=0)
async def list_items(params: ListInput) -> str:
# Make API request with pagination
data = await api_request(limit=params.limit, offset=params.offset)
# Return pagination info
response = {
"total": data["total"],
"count": len(data["items"]),
"offset": params.offset,
"items": data["items"],
"has_more": data["total"] > params.offset + len(data["items"]),
"next_offset": params.offset + len(data["items"]) if data["total"] > params.offset + len(data["items"]) else None
}
return json.dumps(response, indent=2)
```
## Error Handling
Provide clear, actionable error messages:
```python
def _handle_api_error(e: Exception) -> str:
'''Consistent error formatting across all tools.'''
if isinstance(e, httpx.HTTPStatusError):
if e.response.status_code == 404:
return "Error: Resource not found. Please check the ID is correct."
elif e.response.status_code == 403:
return "Error: Permission denied. You don't have access to this resource."
elif e.response.status_code == 429:
return "Error: Rate limit exceeded. Please wait before making more requests."
return f"Error: API request failed with status {e.response.status_code}"
elif isinstance(e, httpx.TimeoutException):
return "Error: Request timed out. Please try again."
return f"Error: Unexpected error occurred: {type(e).__name__}"
```
## Shared Utilities
Extract common functionality into reusable functions:
```python
# Shared API request function
async def _make_api_request(endpoint: str, method: str = "GET", **kwargs) -> dict:
'''Reusable function for all API calls.'''
async with httpx.AsyncClient() as client:
response = await client.request(
method,
f"{API_BASE_URL}/{endpoint}",
timeout=30.0,
**kwargs
)
response.raise_for_status()
return response.json()
```
## Async/Await Best Practices
Always use async/await for network requests and I/O operations:
```python
# Good: Async network request
async def fetch_data(resource_id: str) -> dict:
async with httpx.AsyncClient() as client:
response = await client.get(f"{API_URL}/resource/{resource_id}")
response.raise_for_status()
return response.json()
# Bad: Synchronous request
def fetch_data(resource_id: str) -> dict:
response = requests.get(f"{API_URL}/resource/{resource_id}") # Blocks
return response.json()
```
## Type Hints
Use type hints throughout:
```python
from typing import Optional, List, Dict, Any
async def get_user(user_id: str) -> Dict[str, Any]:
data = await fetch_user(user_id)
return {"id": data["id"], "name": data["name"]}
```
## Tool Docstrings
Every tool must have comprehensive docstrings with explicit type information:
```python
async def search_users(params: UserSearchInput) -> str:
'''
Search for users in the Example system by name, email, or team.
This tool searches across all user profiles in the Example platform,
supporting partial matches and various search filters. It does NOT
create or modify users, only searches existing ones.
Args:
params (UserSearchInput): Validated input parameters containing:
- query (str): Search string to match against names/emails (e.g., "john", "@example.com", "team:marketing")
- limit (Optional[int]): Maximum results to return, between 1-100 (default: 20)
- offset (Optional[int]): Number of results to skip for pagination (default: 0)
Returns:
str: JSON-formatted string containing search results with the following schema:
Success response:
{
"total": int, # Total number of matches found
"count": int, # Number of results in this response
"offset": int, # Current pagination offset
"users": [
{
"id": str, # User ID (e.g., "U123456789")
"name": str, # Full name (e.g., "John Doe")
"email": str, # Email address (e.g., "john@example.com")
"team": str # Team name (e.g., "Marketing") - optional
}
]
}
Error response:
"Error: <error message>" or "No users found matching '<query>'"
Examples:
- Use when: "Find all marketing team members" -> params with query="team:marketing"
- Use when: "Search for John's account" -> params with query="john"
- Don't use when: You need to create a user (use example_create_user instead)
- Don't use when: You have a user ID and need full details (use example_get_user instead)
Error Handling:
- Input validation errors are handled by Pydantic model
- Returns "Error: Rate limit exceeded" if too many requests (429 status)
- Returns "Error: Invalid API authentication" if API key is invalid (401 status)
- Returns formatted list of results or "No users found matching 'query'"
'''
```
## Complete Example
See below for a complete Python MCP server example:
```python
#!/usr/bin/env python3
'''
MCP Server for Example Service.
This server provides tools to interact with Example API, including user search,
project management, and data export capabilities.
'''
from typing import Optional, List, Dict, Any
from enum import Enum
import httpx
from pydantic import BaseModel, Field, field_validator, ConfigDict
from mcp.server.fastmcp import FastMCP
# Initialize the MCP server
mcp = FastMCP("example_mcp")
# Constants
API_BASE_URL = "https://api.example.com/v1"
# Enums
class ResponseFormat(str, Enum):
'''Output format for tool responses.'''
MARKDOWN = "markdown"
JSON = "json"
# Pydantic Models for Input Validation
class UserSearchInput(BaseModel):
'''Input model for user search operations.'''
model_config = ConfigDict(
str_strip_whitespace=True,
validate_assignment=True
)
query: str = Field(..., description="Search string to match against names/emails", min_length=2, max_length=200)
limit: Optional[int] = Field(default=20, description="Maximum results to return", ge=1, le=100)
offset: Optional[int] = Field(default=0, description="Number of results to skip for pagination", ge=0)
response_format: ResponseFormat = Field(default=ResponseFormat.MARKDOWN, description="Output format")
@field_validator('query')
@classmethod
def validate_query(cls, v: str) -> str:
if not v.strip():
raise ValueError("Query cannot be empty or whitespace only")
return v.strip()
# Shared utility functions
async def _make_api_request(endpoint: str, method: str = "GET", **kwargs) -> dict:
'''Reusable function for all API calls.'''
async with httpx.AsyncClient() as client:
response = await client.request(
method,
f"{API_BASE_URL}/{endpoint}",
timeout=30.0,
**kwargs
)
response.raise_for_status()
return response.json()
def _handle_api_error(e: Exception) -> str:
'''Consistent error formatting across all tools.'''
if isinstance(e, httpx.HTTPStatusError):
if e.response.status_code == 404:
return "Error: Resource not found. Please check the ID is correct."
elif e.response.status_code == 403:
return "Error: Permission denied. You don't have access to this resource."
elif e.response.status_code == 429:
return "Error: Rate limit exceeded. Please wait before making more requests."
return f"Error: API request failed with status {e.response.status_code}"
elif isinstance(e, httpx.TimeoutException):
return "Error: Request timed out. Please try again."
return f"Error: Unexpected error occurred: {type(e).__name__}"
# Tool definitions
@mcp.tool(
name="example_search_users",
annotations={
"title": "Search Example Users",
"readOnlyHint": True,
"destructiveHint": False,
"idempotentHint": True,
"openWorldHint": True
}
)
async def example_search_users(params: UserSearchInput) -> str:
'''Search for users in the Example system by name, email, or team.
[Full docstring as shown above]
'''
try:
# Make API request using validated parameters
data = await _make_api_request(
"users/search",
params={
"q": params.query,
"limit": params.limit,
"offset": params.offset
}
)
users = data.get("users", [])
total = data.get("total", 0)
if not users:
return f"No users found matching '{params.query}'"
# Format response based on requested format
if params.response_format == ResponseFormat.MARKDOWN:
lines = [f"# User Search Results: '{params.query}'", ""]
lines.append(f"Found {total} users (showing {len(users)})")
lines.append("")
for user in users:
lines.append(f"## {user['name']} ({user['id']})")
lines.append(f"- **Email**: {user['email']}")
if user.get('team'):
lines.append(f"- **Team**: {user['team']}")
lines.append("")
return "\n".join(lines)
else:
# Machine-readable JSON format
import json
response = {
"total": total,
"count": len(users),
"offset": params.offset,
"users": users
}
return json.dumps(response, indent=2)
except Exception as e:
return _handle_api_error(e)
if __name__ == "__main__":
mcp.run()
```
---
## Advanced FastMCP Features
### Context Parameter Injection
FastMCP can automatically inject a `Context` parameter into tools for advanced capabilities like logging, progress reporting, resource reading, and user interaction:
```python
from mcp.server.fastmcp import FastMCP, Context
mcp = FastMCP("example_mcp")
@mcp.tool()
async def advanced_search(query: str, ctx: Context) -> str:
'''Advanced tool with context access for logging and progress.'''
# Report progress for long operations
await ctx.report_progress(0.25, "Starting search...")
# Log information for debugging
await ctx.log_info("Processing query", {"query": query, "timestamp": datetime.now()})
# Perform search
results = await search_api(query)
await ctx.report_progress(0.75, "Formatting results...")
# Access server configuration
server_name = ctx.fastmcp.name
return format_results(results)
@mcp.tool()
async def interactive_tool(resource_id: str, ctx: Context) -> str:
'''Tool that can request additional input from users.'''
# Request sensitive information when needed
api_key = await ctx.elicit(
prompt="Please provide your API key:",
input_type="password"
)
# Use the provided key
return await api_call(resource_id, api_key)
```
**Context capabilities:**
- `ctx.report_progress(progress, message)` - Report progress for long operations
- `ctx.log_info(message, data)` / `ctx.log_error()` / `ctx.log_debug()` - Logging
- `ctx.elicit(prompt, input_type)` - Request input from users
- `ctx.fastmcp.name` - Access server configuration
- `ctx.read_resource(uri)` - Read MCP resources
### Resource Registration
Expose data as resources for efficient, template-based access:
```python
@mcp.resource("file://documents/{name}")
async def get_document(name: str) -> str:
'''Expose documents as MCP resources.
Resources are useful for static or semi-static data that doesn't
require complex parameters. They use URI templates for flexible access.
'''
document_path = f"./docs/{name}"
with open(document_path, "r") as f:
return f.read()
@mcp.resource("config://settings/{key}")
async def get_setting(key: str, ctx: Context) -> str:
'''Expose configuration as resources with context.'''
settings = await load_settings()
return json.dumps(settings.get(key, {}))
```
**When to use Resources vs Tools:**
- **Resources**: For data access with simple parameters (URI templates)
- **Tools**: For complex operations with validation and business logic
### Structured Output Types
FastMCP supports multiple return types beyond strings:
```python
from typing import TypedDict
from dataclasses import dataclass
from pydantic import BaseModel
# TypedDict for structured returns
class UserData(TypedDict):
id: str
name: str
email: str
@mcp.tool()
async def get_user_typed(user_id: str) -> UserData:
'''Returns structured data - FastMCP handles serialization.'''
return {"id": user_id, "name": "John Doe", "email": "john@example.com"}
# Pydantic models for complex validation
class DetailedUser(BaseModel):
id: str
name: str
email: str
created_at: datetime
metadata: Dict[str, Any]
@mcp.tool()
async def get_user_detailed(user_id: str) -> DetailedUser:
'''Returns Pydantic model - automatically generates schema.'''
user = await fetch_user(user_id)
return DetailedUser(**user)
```
### Lifespan Management
Initialize resources that persist across requests:
```python
from contextlib import asynccontextmanager
@asynccontextmanager
async def app_lifespan():
'''Manage resources that live for the server's lifetime.'''
# Initialize connections, load config, etc.
db = await connect_to_database()
config = load_configuration()
# Make available to all tools
yield {"db": db, "config": config}
# Cleanup on shutdown
await db.close()
mcp = FastMCP("example_mcp", lifespan=app_lifespan)
@mcp.tool()
async def query_data(query: str, ctx: Context) -> str:
'''Access lifespan resources through context.'''
db = ctx.request_context.lifespan_state["db"]
results = await db.query(query)
return format_results(results)
```
### Transport Options
FastMCP supports two main transport mechanisms:
```python
# stdio transport (for local tools) - default
if __name__ == "__main__":
mcp.run()
# Streamable HTTP transport (for remote servers)
if __name__ == "__main__":
mcp.run(transport="streamable_http", port=8000)
```
**Transport selection:**
- **stdio**: Command-line tools, local integrations, subprocess execution
- **Streamable HTTP**: Web services, remote access, multiple clients
---
## Code Best Practices
### Code Composability and Reusability
Your implementation MUST prioritize composability and code reuse:
1. **Extract Common Functionality**:
- Create reusable helper functions for operations used across multiple tools
- Build shared API clients for HTTP requests instead of duplicating code
- Centralize error handling logic in utility functions
- Extract business logic into dedicated functions that can be composed
- Extract shared markdown or JSON field selection & formatting functionality
2. **Avoid Duplication**:
- NEVER copy-paste similar code between tools
- If you find yourself writing similar logic twice, extract it into a function
- Common operations like pagination, filtering, field selection, and formatting should be shared
- Authentication/authorization logic should be centralized
### Python-Specific Best Practices
1. **Use Type Hints**: Always include type annotations for function parameters and return values
2. **Pydantic Models**: Define clear Pydantic models for all input validation
3. **Avoid Manual Validation**: Let Pydantic handle input validation with constraints
4. **Proper Imports**: Group imports (standard library, third-party, local)
5. **Error Handling**: Use specific exception types (httpx.HTTPStatusError, not generic Exception)
6. **Async Context Managers**: Use `async with` for resources that need cleanup
7. **Constants**: Define module-level constants in UPPER_CASE
## Quality Checklist
Before finalizing your Python MCP server implementation, ensure:
### Strategic Design
- [ ] Tools enable complete workflows, not just API endpoint wrappers
- [ ] Tool names reflect natural task subdivisions
- [ ] Response formats optimize for agent context efficiency
- [ ] Human-readable identifiers used where appropriate
- [ ] Error messages guide agents toward correct usage
### Implementation Quality
- [ ] FOCUSED IMPLEMENTATION: Most important and valuable tools implemented
- [ ] All tools have descriptive names and documentation
- [ ] Return types are consistent across similar operations
- [ ] Error handling is implemented for all external calls
- [ ] Server name follows format: `{service}_mcp`
- [ ] All network operations use async/await
- [ ] Common functionality is extracted into reusable functions
- [ ] Error messages are clear, actionable, and educational
- [ ] Outputs are properly validated and formatted
### Tool Configuration
- [ ] All tools implement 'name' and 'annotations' in the decorator
- [ ] Annotations correctly set (readOnlyHint, destructiveHint, idempotentHint, openWorldHint)
- [ ] All tools use Pydantic BaseModel for input validation with Field() definitions
- [ ] All Pydantic Fields have explicit types and descriptions with constraints
- [ ] All tools have comprehensive docstrings with explicit input/output types
- [ ] Docstrings include complete schema structure for dict/JSON returns
- [ ] Pydantic models handle input validation (no manual validation needed)
### Advanced Features (where applicable)
- [ ] Context injection used for logging, progress, or elicitation
- [ ] Resources registered for appropriate data endpoints
- [ ] Lifespan management implemented for persistent connections
- [ ] Structured output types used (TypedDict, Pydantic models)
- [ ] Appropriate transport configured (stdio or streamable HTTP)
### Code Quality
- [ ] File includes proper imports including Pydantic imports
- [ ] Pagination is properly implemented where applicable
- [ ] Filtering options are provided for potentially large result sets
- [ ] All async functions are properly defined with `async def`
- [ ] HTTP client usage follows async patterns with proper context managers
- [ ] Type hints are used throughout the code
- [ ] Constants are defined at module level in UPPER_CASE
### Testing
- [ ] Server runs successfully: `python your_server.py --help`
- [ ] All imports resolve correctly
- [ ] Sample tool calls work as expected
- [ ] Error scenarios handled gracefully
skills/mcp-builder/scripts/connections.py
+151
View File
@@ -0,0 +1,151 @@
"""Lightweight connection handling for MCP servers."""
from abc import ABC, abstractmethod
from contextlib import AsyncExitStack
from typing import Any
from mcp import ClientSession, StdioServerParameters
from mcp.client.sse import sse_client
from mcp.client.stdio import stdio_client
from mcp.client.streamable_http import streamablehttp_client
class MCPConnection(ABC):
"""Base class for MCP server connections."""
def __init__(self):
self.session = None
self._stack = None
@abstractmethod
def _create_context(self):
"""Create the connection context based on connection type."""
async def __aenter__(self):
"""Initialize MCP server connection."""
self._stack = AsyncExitStack()
await self._stack.__aenter__()
try:
ctx = self._create_context()
result = await self._stack.enter_async_context(ctx)
if len(result) == 2:
read, write = result
elif len(result) == 3:
read, write, _ = result
else:
raise ValueError(f"Unexpected context result: {result}")
session_ctx = ClientSession(read, write)
self.session = await self._stack.enter_async_context(session_ctx)
await self.session.initialize()
return self
except BaseException:
await self._stack.__aexit__(None, None, None)
raise
async def __aexit__(self, exc_type, exc_val, exc_tb):
"""Clean up MCP server connection resources."""
if self._stack:
await self._stack.__aexit__(exc_type, exc_val, exc_tb)
self.session = None
self._stack = None
async def list_tools(self) -> list[dict[str, Any]]:
"""Retrieve available tools from the MCP server."""
response = await self.session.list_tools()
return [
{
"name": tool.name,
"description": tool.description,
"input_schema": tool.inputSchema,
}
for tool in response.tools
]
async def call_tool(self, tool_name: str, arguments: dict[str, Any]) -> Any:
"""Call a tool on the MCP server with provided arguments."""
result = await self.session.call_tool(tool_name, arguments=arguments)
return result.content
class MCPConnectionStdio(MCPConnection):
"""MCP connection using standard input/output."""
def __init__(self, command: str, args: list[str] = None, env: dict[str, str] = None):
super().__init__()
self.command = command
self.args = args or []
self.env = env
def _create_context(self):
return stdio_client(
StdioServerParameters(command=self.command, args=self.args, env=self.env)
)
class MCPConnectionSSE(MCPConnection):
"""MCP connection using Server-Sent Events."""
def __init__(self, url: str, headers: dict[str, str] = None):
super().__init__()
self.url = url
self.headers = headers or {}
def _create_context(self):
return sse_client(url=self.url, headers=self.headers)
class MCPConnectionHTTP(MCPConnection):
"""MCP connection using Streamable HTTP."""
def __init__(self, url: str, headers: dict[str, str] = None):
super().__init__()
self.url = url
self.headers = headers or {}
def _create_context(self):
return streamablehttp_client(url=self.url, headers=self.headers)
def create_connection(
transport: str,
command: str = None,
args: list[str] = None,
env: dict[str, str] = None,
url: str = None,
headers: dict[str, str] = None,
) -> MCPConnection:
"""Factory function to create the appropriate MCP connection.
Args:
transport: Connection type ("stdio", "sse", or "http")
command: Command to run (stdio only)
args: Command arguments (stdio only)
env: Environment variables (stdio only)
url: Server URL (sse and http only)
headers: HTTP headers (sse and http only)
Returns:
MCPConnection instance
"""
transport = transport.lower()
if transport == "stdio":
if not command:
raise ValueError("Command is required for stdio transport")
return MCPConnectionStdio(command=command, args=args, env=env)
elif transport == "sse":
if not url:
raise ValueError("URL is required for sse transport")
return MCPConnectionSSE(url=url, headers=headers)
elif transport in ["http", "streamable_http", "streamable-http"]:
if not url:
raise ValueError("URL is required for http transport")
return MCPConnectionHTTP(url=url, headers=headers)
else:
raise ValueError(f"Unsupported transport type: {transport}. Use 'stdio', 'sse', or 'http'")
skills/mcp-builder/scripts/evaluation.py
+373
View File
@@ -0,0 +1,373 @@
"""MCP Server Evaluation Harness
This script evaluates MCP servers by running test questions against them using Claude.
"""
import argparse
import asyncio
import json
import re
import sys
import time
import traceback
import xml.etree.ElementTree as ET
from pathlib import Path
from typing import Any
from anthropic import Anthropic
from connections import create_connection
EVALUATION_PROMPT = """You are an AI assistant with access to tools.
When given a task, you MUST:
1. Use the available tools to complete the task
2. Provide summary of each step in your approach, wrapped in <summary> tags
3. Provide feedback on the tools provided, wrapped in <feedback> tags
4. Provide your final response, wrapped in <response> tags
Summary Requirements:
- In your <summary> tags, you must explain:
- The steps you took to complete the task
- Which tools you used, in what order, and why
- The inputs you provided to each tool
- The outputs you received from each tool
- A summary for how you arrived at the response
Feedback Requirements:
- In your <feedback> tags, provide constructive feedback on the tools:
- Comment on tool names: Are they clear and descriptive?
- Comment on input parameters: Are they well-documented? Are required vs optional parameters clear?
- Comment on descriptions: Do they accurately describe what the tool does?
- Comment on any errors encountered during tool usage: Did the tool fail to execute? Did the tool return too many tokens?
- Identify specific areas for improvement and explain WHY they would help
- Be specific and actionable in your suggestions
Response Requirements:
- Your response should be concise and directly address what was asked
- Always wrap your final response in <response> tags
- If you cannot solve the task return <response>NOT_FOUND</response>
- For numeric responses, provide just the number
- For IDs, provide just the ID
- For names or text, provide the exact text requested
- Your response should go last"""
def parse_evaluation_file(file_path: Path) -> list[dict[str, Any]]:
"""Parse XML evaluation file with qa_pair elements."""
try:
tree = ET.parse(file_path)
root = tree.getroot()
evaluations = []
for qa_pair in root.findall(".//qa_pair"):
question_elem = qa_pair.find("question")
answer_elem = qa_pair.find("answer")
if question_elem is not None and answer_elem is not None:
evaluations.append({
"question": (question_elem.text or "").strip(),
"answer": (answer_elem.text or "").strip(),
})
return evaluations
except Exception as e:
print(f"Error parsing evaluation file {file_path}: {e}")
return []
def extract_xml_content(text: str, tag: str) -> str | None:
"""Extract content from XML tags."""
pattern = rf"<{tag}>(.*?)</{tag}>"
matches = re.findall(pattern, text, re.DOTALL)
return matches[-1].strip() if matches else None
async def agent_loop(
client: Anthropic,
model: str,
question: str,
tools: list[dict[str, Any]],
connection: Any,
) -> tuple[str, dict[str, Any]]:
"""Run the agent loop with MCP tools."""
messages = [{"role": "user", "content": question}]
response = await asyncio.to_thread(
client.messages.create,
model=model,
max_tokens=4096,
system=EVALUATION_PROMPT,
messages=messages,
tools=tools,
)
messages.append({"role": "assistant", "content": response.content})
tool_metrics = {}
while response.stop_reason == "tool_use":
tool_use = next(block for block in response.content if block.type == "tool_use")
tool_name = tool_use.name
tool_input = tool_use.input
tool_start_ts = time.time()
try:
tool_result = await connection.call_tool(tool_name, tool_input)
tool_response = json.dumps(tool_result) if isinstance(tool_result, (dict, list)) else str(tool_result)
except Exception as e:
tool_response = f"Error executing tool {tool_name}: {str(e)}\n"
tool_response += traceback.format_exc()
tool_duration = time.time() - tool_start_ts
if tool_name not in tool_metrics:
tool_metrics[tool_name] = {"count": 0, "durations": []}
tool_metrics[tool_name]["count"] += 1
tool_metrics[tool_name]["durations"].append(tool_duration)
messages.append({
"role": "user",
"content": [{
"type": "tool_result",
"tool_use_id": tool_use.id,
"content": tool_response,
}]
})
response = await asyncio.to_thread(
client.messages.create,
model=model,
max_tokens=4096,
system=EVALUATION_PROMPT,
messages=messages,
tools=tools,
)
messages.append({"role": "assistant", "content": response.content})
response_text = next(
(block.text for block in response.content if hasattr(block, "text")),
None,
)
return response_text, tool_metrics
async def evaluate_single_task(
client: Anthropic,
model: str,
qa_pair: dict[str, Any],
tools: list[dict[str, Any]],
connection: Any,
task_index: int,
) -> dict[str, Any]:
"""Evaluate a single QA pair with the given tools."""
start_time = time.time()
print(f"Task {task_index + 1}: Running task with question: {qa_pair['question']}")
response, tool_metrics = await agent_loop(client, model, qa_pair["question"], tools, connection)
response_value = extract_xml_content(response, "response")
summary = extract_xml_content(response, "summary")
feedback = extract_xml_content(response, "feedback")
duration_seconds = time.time() - start_time
return {
"question": qa_pair["question"],
"expected": qa_pair["answer"],
"actual": response_value,
"score": int(response_value == qa_pair["answer"]) if response_value else 0,
"total_duration": duration_seconds,
"tool_calls": tool_metrics,
"num_tool_calls": sum(len(metrics["durations"]) for metrics in tool_metrics.values()),
"summary": summary,
"feedback": feedback,
}
REPORT_HEADER = """
# Evaluation Report
## Summary
- **Accuracy**: {correct}/{total} ({accuracy:.1f}%)
- **Average Task Duration**: {average_duration_s:.2f}s
- **Average Tool Calls per Task**: {average_tool_calls:.2f}
- **Total Tool Calls**: {total_tool_calls}
---
"""
TASK_TEMPLATE = """
### Task {task_num}
**Question**: {question}
**Ground Truth Answer**: `{expected_answer}`
**Actual Answer**: `{actual_answer}`
**Correct**: {correct_indicator}
**Duration**: {total_duration:.2f}s
**Tool Calls**: {tool_calls}
**Summary**
{summary}
**Feedback**
{feedback}
---
"""
async def run_evaluation(
eval_path: Path,
connection: Any,
model: str = "claude-3-7-sonnet-20250219",
) -> str:
"""Run evaluation with MCP server tools."""
print("🚀 Starting Evaluation")
client = Anthropic()
tools = await connection.list_tools()
print(f"📋 Loaded {len(tools)} tools from MCP server")
qa_pairs = parse_evaluation_file(eval_path)
print(f"📋 Loaded {len(qa_pairs)} evaluation tasks")
results = []
for i, qa_pair in enumerate(qa_pairs):
print(f"Processing task {i + 1}/{len(qa_pairs)}")
result = await evaluate_single_task(client, model, qa_pair, tools, connection, i)
results.append(result)
correct = sum(r["score"] for r in results)
accuracy = (correct / len(results)) * 100 if results else 0
average_duration_s = sum(r["total_duration"] for r in results) / len(results) if results else 0
average_tool_calls = sum(r["num_tool_calls"] for r in results) / len(results) if results else 0
total_tool_calls = sum(r["num_tool_calls"] for r in results)
report = REPORT_HEADER.format(
correct=correct,
total=len(results),
accuracy=accuracy,
average_duration_s=average_duration_s,
average_tool_calls=average_tool_calls,
total_tool_calls=total_tool_calls,
)
report += "".join([
TASK_TEMPLATE.format(
task_num=i + 1,
question=qa_pair["question"],
expected_answer=qa_pair["answer"],
actual_answer=result["actual"] or "N/A",
correct_indicator="✅" if result["score"] else "❌",
total_duration=result["total_duration"],
tool_calls=json.dumps(result["tool_calls"], indent=2),
summary=result["summary"] or "N/A",
feedback=result["feedback"] or "N/A",
)
for i, (qa_pair, result) in enumerate(zip(qa_pairs, results))
])
return report
def parse_headers(header_list: list[str]) -> dict[str, str]:
"""Parse header strings in format 'Key: Value' into a dictionary."""
headers = {}
if not header_list:
return headers
for header in header_list:
if ":" in header:
key, value = header.split(":", 1)
headers[key.strip()] = value.strip()
else:
print(f"Warning: Ignoring malformed header: {header}")
return headers
def parse_env_vars(env_list: list[str]) -> dict[str, str]:
"""Parse environment variable strings in format 'KEY=VALUE' into a dictionary."""
env = {}
if not env_list:
return env
for env_var in env_list:
if "=" in env_var:
key, value = env_var.split("=", 1)
env[key.strip()] = value.strip()
else:
print(f"Warning: Ignoring malformed environment variable: {env_var}")
return env
async def main():
parser = argparse.ArgumentParser(
description="Evaluate MCP servers using test questions",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
# Evaluate a local stdio MCP server
python evaluation.py -t stdio -c python -a my_server.py eval.xml
# Evaluate an SSE MCP server
python evaluation.py -t sse -u https://example.com/mcp -H "Authorization: Bearer token" eval.xml
# Evaluate an HTTP MCP server with custom model
python evaluation.py -t http -u https://example.com/mcp -m claude-3-5-sonnet-20241022 eval.xml
""",
)
parser.add_argument("eval_file", type=Path, help="Path to evaluation XML file")
parser.add_argument("-t", "--transport", choices=["stdio", "sse", "http"], default="stdio", help="Transport type (default: stdio)")
parser.add_argument("-m", "--model", default="claude-3-7-sonnet-20250219", help="Claude model to use (default: claude-3-7-sonnet-20250219)")
stdio_group = parser.add_argument_group("stdio options")
stdio_group.add_argument("-c", "--command", help="Command to run MCP server (stdio only)")
stdio_group.add_argument("-a", "--args", nargs="+", help="Arguments for the command (stdio only)")
stdio_group.add_argument("-e", "--env", nargs="+", help="Environment variables in KEY=VALUE format (stdio only)")
remote_group = parser.add_argument_group("sse/http options")
remote_group.add_argument("-u", "--url", help="MCP server URL (sse/http only)")
remote_group.add_argument("-H", "--header", nargs="+", dest="headers", help="HTTP headers in 'Key: Value' format (sse/http only)")
parser.add_argument("-o", "--output", type=Path, help="Output file for evaluation report (default: stdout)")
args = parser.parse_args()
if not args.eval_file.exists():
print(f"Error: Evaluation file not found: {args.eval_file}")
sys.exit(1)
headers = parse_headers(args.headers) if args.headers else None
env_vars = parse_env_vars(args.env) if args.env else None
try:
connection = create_connection(
transport=args.transport,
command=args.command,
args=args.args,
env=env_vars,
url=args.url,
headers=headers,
)
except ValueError as e:
print(f"Error: {e}")
sys.exit(1)
print(f"🔗 Connecting to MCP server via {args.transport}...")
async with connection:
print("✅ Connected successfully")
report = await run_evaluation(args.eval_file, connection, args.model)
if args.output:
args.output.write_text(report)
print(f"\n✅ Report saved to {args.output}")
else:
print("\n" + report)
if __name__ == "__main__":
asyncio.run(main())
skills/mcp-builder/scripts/example_evaluation.xml
+22
View File
@@ -0,0 +1,22 @@
<evaluation>
<qa_pair>
<question>Calculate the compound interest on $10,000 invested at 5% annual interest rate, compounded monthly for 3 years. What is the final amount in dollars (rounded to 2 decimal places)?</question>
<answer>11614.72</answer>
</qa_pair>
<qa_pair>
<question>A projectile is launched at a 45-degree angle with an initial velocity of 50 m/s. Calculate the total distance (in meters) it has traveled from the launch point after 2 seconds, assuming g=9.8 m/s². Round to 2 decimal places.</question>
<answer>87.25</answer>
</qa_pair>
<qa_pair>
<question>A sphere has a volume of 500 cubic meters. Calculate its surface area in square meters. Round to 2 decimal places.</question>
<answer>304.65</answer>
</qa_pair>
<qa_pair>
<question>Calculate the population standard deviation of this dataset: [12, 15, 18, 22, 25, 30, 35]. Round to 2 decimal places.</question>
<answer>7.61</answer>
</qa_pair>
<qa_pair>
<question>Calculate the pH of a solution with a hydrogen ion concentration of 3.5 × 10^-5 M. Round to 2 decimal places.</question>
<answer>4.46</answer>
</qa_pair>
</evaluation>
skills/mcp-builder/scripts/requirements.txt
+2
View File
@@ -0,0 +1,2 @@
anthropic>=0.39.0
mcp>=1.1.0
skills/skill-creator/LICENSE.txt
+202
View File
@@ -0,0 +1,202 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright 2026 Anthropic, PBC.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
skills/skill-creator/SKILL.md
+485
View File
@@ -0,0 +1,485 @@
---
name: skill-creator
description: Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
---
# Skill Creator
A skill for creating new skills and iteratively improving them.
At a high level, the process of creating a skill goes like this:
- Decide what you want the skill to do and roughly how it should do it
- Write a draft of the skill
- Create a few test prompts and run claude-with-access-to-the-skill on them
- Help the user evaluate the results both qualitatively and quantitatively
- While the runs happen in the background, draft some quantitative evals if there aren't any (if there are some, you can either use as is or modify if you feel something needs to change about them). Then explain them to the user (or if they already existed, explain the ones that already exist)
- Use the `eval-viewer/generate_review.py` script to show the user the results for them to look at, and also let them look at the quantitative metrics
- Rewrite the skill based on feedback from the user's evaluation of the results (and also if there are any glaring flaws that become apparent from the quantitative benchmarks)
- Repeat until you're satisfied
- Expand the test set and try again at larger scale
Your job when using this skill is to figure out where the user is in this process and then jump in and help them progress through these stages. So for instance, maybe they're like "I want to make a skill for X". You can help narrow down what they mean, write a draft, write the test cases, figure out how they want to evaluate, run all the prompts, and repeat.
On the other hand, maybe they already have a draft of the skill. In this case you can go straight to the eval/iterate part of the loop.
Of course, you should always be flexible and if the user is like "I don't need to run a bunch of evaluations, just vibe with me", you can do that instead.
Then after the skill is done (but again, the order is flexible), you can also run the skill description improver, which we have a whole separate script for, to optimize the triggering of the skill.
Cool? Cool.
## Communicating with the user
The skill creator is liable to be used by people across a wide range of familiarity with coding jargon. If you haven't heard (and how could you, it's only very recently that it started), there's a trend now where the power of Claude is inspiring plumbers to open up their terminals, parents and grandparents to google "how to install npm". On the other hand, the bulk of users are probably fairly computer-literate.
So please pay attention to context cues to understand how to phrase your communication! In the default case, just to give you some idea:
- "evaluation" and "benchmark" are borderline, but OK
- for "JSON" and "assertion" you want to see serious cues from the user that they know what those things are before using them without explaining them
It's OK to briefly explain terms if you're in doubt, and feel free to clarify terms with a short definition if you're unsure if the user will get it.
---
## Creating a skill
### Capture Intent
Start by understanding the user's intent. The current conversation might already contain a workflow the user wants to capture (e.g., they say "turn this into a skill"). If so, extract answers from the conversation history first — the tools used, the sequence of steps, corrections the user made, input/output formats observed. The user may need to fill the gaps, and should confirm before proceeding to the next step.
1. What should this skill enable Claude to do?
2. When should this skill trigger? (what user phrases/contexts)
3. What's the expected output format?
4. Should we set up test cases to verify the skill works? Skills with objectively verifiable outputs (file transforms, data extraction, code generation, fixed workflow steps) benefit from test cases. Skills with subjective outputs (writing style, art) often don't need them. Suggest the appropriate default based on the skill type, but let the user decide.
### Interview and Research
Proactively ask questions about edge cases, input/output formats, example files, success criteria, and dependencies. Wait to write test prompts until you've got this part ironed out.
Check available MCPs - if useful for research (searching docs, finding similar skills, looking up best practices), research in parallel via subagents if available, otherwise inline. Come prepared with context to reduce burden on the user.
### Write the SKILL.md
Based on the user interview, fill in these components:
- **name**: Skill identifier
- **description**: When to trigger, what it does. This is the primary triggering mechanism - include both what the skill does AND specific contexts for when to use it. All "when to use" info goes here, not in the body. Note: currently Claude has a tendency to "undertrigger" skills -- to not use them when they'd be useful. To combat this, please make the skill descriptions a little bit "pushy". So for instance, instead of "How to build a simple fast dashboard to display internal Anthropic data.", you might write "How to build a simple fast dashboard to display internal Anthropic data. Make sure to use this skill whenever the user mentions dashboards, data visualization, internal metrics, or wants to display any kind of company data, even if they don't explicitly ask for a 'dashboard.'"
- **compatibility**: Required tools, dependencies (optional, rarely needed)
- **the rest of the skill :)**
### Skill Writing Guide
#### Anatomy of a Skill
```
skill-name/
├── SKILL.md (required)
│ ├── YAML frontmatter (name, description required)
│ └── Markdown instructions
└── Bundled Resources (optional)
├── scripts/ - Executable code for deterministic/repetitive tasks
├── references/ - Docs loaded into context as needed
└── assets/ - Files used in output (templates, icons, fonts)
```
#### Progressive Disclosure
Skills use a three-level loading system:
1. **Metadata** (name + description) - Always in context (~100 words)
2. **SKILL.md body** - In context whenever skill triggers (<500 lines ideal)
3. **Bundled resources** - As needed (unlimited, scripts can execute without loading)
These word counts are approximate and you can feel free to go longer if needed.
**Key patterns:**
- Keep SKILL.md under 500 lines; if you're approaching this limit, add an additional layer of hierarchy along with clear pointers about where the model using the skill should go next to follow up.
- Reference files clearly from SKILL.md with guidance on when to read them
- For large reference files (>300 lines), include a table of contents
**Domain organization**: When a skill supports multiple domains/frameworks, organize by variant:
```
cloud-deploy/
├── SKILL.md (workflow + selection)
└── references/
├── aws.md
├── gcp.md
└── azure.md
```
Claude reads only the relevant reference file.
#### Principle of Lack of Surprise
This goes without saying, but skills must not contain malware, exploit code, or any content that could compromise system security. A skill's contents should not surprise the user in their intent if described. Don't go along with requests to create misleading skills or skills designed to facilitate unauthorized access, data exfiltration, or other malicious activities. Things like a "roleplay as an XYZ" are OK though.
#### Writing Patterns
Prefer using the imperative form in instructions.
**Defining output formats** - You can do it like this:
```markdown
## Report structure
ALWAYS use this exact template:
# [Title]
## Executive summary
## Key findings
## Recommendations
```
**Examples pattern** - It's useful to include examples. You can format them like this (but if "Input" and "Output" are in the examples you might want to deviate a little):
```markdown
## Commit message format
**Example 1:**
Input: Added user authentication with JWT tokens
Output: feat(auth): implement JWT-based authentication
```
### Writing Style
Try to explain to the model why things are important in lieu of heavy-handed musty MUSTs. Use theory of mind and try to make the skill general and not super-narrow to specific examples. Start by writing a draft and then look at it with fresh eyes and improve it.
### Test Cases
After writing the skill draft, come up with 2-3 realistic test prompts — the kind of thing a real user would actually say. Share them with the user: [you don't have to use this exact language] "Here are a few test cases I'd like to try. Do these look right, or do you want to add more?" Then run them.
Save test cases to `evals/evals.json`. Don't write assertions yet — just the prompts. You'll draft assertions in the next step while the runs are in progress.
```json
{
"skill_name": "example-skill",
"evals": [
{
"id": 1,
"prompt": "User's task prompt",
"expected_output": "Description of expected result",
"files": []
}
]
}
```
See `references/schemas.md` for the full schema (including the `assertions` field, which you'll add later).
## Running and evaluating test cases
This section is one continuous sequence — don't stop partway through. Do NOT use `/skill-test` or any other testing skill.
Put results in `<skill-name>-workspace/` as a sibling to the skill directory. Within the workspace, organize results by iteration (`iteration-1/`, `iteration-2/`, etc.) and within that, each test case gets a directory (`eval-0/`, `eval-1/`, etc.). Don't create all of this upfront — just create directories as you go.
### Step 1: Spawn all runs (with-skill AND baseline) in the same turn
For each test case, spawn two subagents in the same turn — one with the skill, one without. This is important: don't spawn the with-skill runs first and then come back for baselines later. Launch everything at once so it all finishes around the same time.
**With-skill run:**
```
Execute this task:
- Skill path: <path-to-skill>
- Task: <eval prompt>
- Input files: <eval files if any, or "none">
- Save outputs to: <workspace>/iteration-<N>/eval-<ID>/with_skill/outputs/
- Outputs to save: <what the user cares about — e.g., "the .docx file", "the final CSV">
```
**Baseline run** (same prompt, but the baseline depends on context):
- **Creating a new skill**: no skill at all. Same prompt, no skill path, save to `without_skill/outputs/`.
- **Improving an existing skill**: the old version. Before editing, snapshot the skill (`cp -r <skill-path> <workspace>/skill-snapshot/`), then point the baseline subagent at the snapshot. Save to `old_skill/outputs/`.
Write an `eval_metadata.json` for each test case (assertions can be empty for now). Give each eval a descriptive name based on what it's testing — not just "eval-0". Use this name for the directory too. If this iteration uses new or modified eval prompts, create these files for each new eval directory — don't assume they carry over from previous iterations.
```json
{
"eval_id": 0,
"eval_name": "descriptive-name-here",
"prompt": "The user's task prompt",
"assertions": []
}
```
### Step 2: While runs are in progress, draft assertions
Don't just wait for the runs to finish — you can use this time productively. Draft quantitative assertions for each test case and explain them to the user. If assertions already exist in `evals/evals.json`, review them and explain what they check.
Good assertions are objectively verifiable and have descriptive names — they should read clearly in the benchmark viewer so someone glancing at the results immediately understands what each one checks. Subjective skills (writing style, design quality) are better evaluated qualitatively — don't force assertions onto things that need human judgment.
Update the `eval_metadata.json` files and `evals/evals.json` with the assertions once drafted. Also explain to the user what they'll see in the viewer — both the qualitative outputs and the quantitative benchmark.
### Step 3: As runs complete, capture timing data
When each subagent task completes, you receive a notification containing `total_tokens` and `duration_ms`. Save this data immediately to `timing.json` in the run directory:
```json
{
"total_tokens": 84852,
"duration_ms": 23332,
"total_duration_seconds": 23.3
}
```
This is the only opportunity to capture this data — it comes through the task notification and isn't persisted elsewhere. Process each notification as it arrives rather than trying to batch them.
### Step 4: Grade, aggregate, and launch the viewer
Once all runs are done:
1. **Grade each run** — spawn a grader subagent (or grade inline) that reads `agents/grader.md` and evaluates each assertion against the outputs. Save results to `grading.json` in each run directory. The grading.json expectations array must use the fields `text`, `passed`, and `evidence` (not `name`/`met`/`details` or other variants) — the viewer depends on these exact field names. For assertions that can be checked programmatically, write and run a script rather than eyeballing it — scripts are faster, more reliable, and can be reused across iterations.
2. **Aggregate into benchmark** — run the aggregation script from the skill-creator directory:
```bash
python -m scripts.aggregate_benchmark <workspace>/iteration-N --skill-name <name>
```
This produces `benchmark.json` and `benchmark.md` with pass_rate, time, and tokens for each configuration, with mean ± stddev and the delta. If generating benchmark.json manually, see `references/schemas.md` for the exact schema the viewer expects.
Put each with_skill version before its baseline counterpart.
3. **Do an analyst pass** — read the benchmark data and surface patterns the aggregate stats might hide. See `agents/analyzer.md` (the "Analyzing Benchmark Results" section) for what to look for — things like assertions that always pass regardless of skill (non-discriminating), high-variance evals (possibly flaky), and time/token tradeoffs.
4. **Launch the viewer** with both qualitative outputs and quantitative data:
```bash
nohup python <skill-creator-path>/eval-viewer/generate_review.py \
<workspace>/iteration-N \
--skill-name "my-skill" \
--benchmark <workspace>/iteration-N/benchmark.json \
> /dev/null 2>&1 &
VIEWER_PID=$!
```
For iteration 2+, also pass `--previous-workspace <workspace>/iteration-<N-1>`.
**Cowork / headless environments:** If `webbrowser.open()` is not available or the environment has no display, use `--static <output_path>` to write a standalone HTML file instead of starting a server. Feedback will be downloaded as a `feedback.json` file when the user clicks "Submit All Reviews". After download, copy `feedback.json` into the workspace directory for the next iteration to pick up.
Note: please use generate_review.py to create the viewer; there's no need to write custom HTML.
5. **Tell the user** something like: "I've opened the results in your browser. There are two tabs — 'Outputs' lets you click through each test case and leave feedback, 'Benchmark' shows the quantitative comparison. When you're done, come back here and let me know."
### What the user sees in the viewer
The "Outputs" tab shows one test case at a time:
- **Prompt**: the task that was given
- **Output**: the files the skill produced, rendered inline where possible
- **Previous Output** (iteration 2+): collapsed section showing last iteration's output
- **Formal Grades** (if grading was run): collapsed section showing assertion pass/fail
- **Feedback**: a textbox that auto-saves as they type
- **Previous Feedback** (iteration 2+): their comments from last time, shown below the textbox
The "Benchmark" tab shows the stats summary: pass rates, timing, and token usage for each configuration, with per-eval breakdowns and analyst observations.
Navigation is via prev/next buttons or arrow keys. When done, they click "Submit All Reviews" which saves all feedback to `feedback.json`.
### Step 5: Read the feedback
When the user tells you they're done, read `feedback.json`:
```json
{
"reviews": [
{"run_id": "eval-0-with_skill", "feedback": "the chart is missing axis labels", "timestamp": "..."},
{"run_id": "eval-1-with_skill", "feedback": "", "timestamp": "..."},
{"run_id": "eval-2-with_skill", "feedback": "perfect, love this", "timestamp": "..."}
],
"status": "complete"
}
```
Empty feedback means the user thought it was fine. Focus your improvements on the test cases where the user had specific complaints.
Kill the viewer server when you're done with it:
```bash
kill $VIEWER_PID 2>/dev/null
```
---
## Improving the skill
This is the heart of the loop. You've run the test cases, the user has reviewed the results, and now you need to make the skill better based on their feedback.
### How to think about improvements
1. **Generalize from the feedback.** The big picture thing that's happening here is that we're trying to create skills that can be used a million times (maybe literally, maybe even more who knows) across many different prompts. Here you and the user are iterating on only a few examples over and over again because it helps move faster. The user knows these examples in and out and it's quick for them to assess new outputs. But if the skill you and the user are codeveloping works only for those examples, it's useless. Rather than put in fiddly overfitty changes, or oppressively constrictive MUSTs, if there's some stubborn issue, you might try branching out and using different metaphors, or recommending different patterns of working. It's relatively cheap to try and maybe you'll land on something great.
2. **Keep the prompt lean.** Remove things that aren't pulling their weight. Make sure to read the transcripts, not just the final outputs — if it looks like the skill is making the model waste a bunch of time doing things that are unproductive, you can try getting rid of the parts of the skill that are making it do that and seeing what happens.
3. **Explain the why.** Try hard to explain the **why** behind everything you're asking the model to do. Today's LLMs are *smart*. They have good theory of mind and when given a good harness can go beyond rote instructions and really make things happen. Even if the feedback from the user is terse or frustrated, try to actually understand the task and why the user is writing what they wrote, and what they actually wrote, and then transmit this understanding into the instructions. If you find yourself writing ALWAYS or NEVER in all caps, or using super rigid structures, that's a yellow flag — if possible, reframe and explain the reasoning so that the model understands why the thing you're asking for is important. That's a more humane, powerful, and effective approach.
4. **Look for repeated work across test cases.** Read the transcripts from the test runs and notice if the subagents all independently wrote similar helper scripts or took the same multi-step approach to something. If all 3 test cases resulted in the subagent writing a `create_docx.py` or a `build_chart.py`, that's a strong signal the skill should bundle that script. Write it once, put it in `scripts/`, and tell the skill to use it. This saves every future invocation from reinventing the wheel.
This task is pretty important (we are trying to create billions a year in economic value here!) and your thinking time is not the blocker; take your time and really mull things over. I'd suggest writing a draft revision and then looking at it anew and making improvements. Really do your best to get into the head of the user and understand what they want and need.
### The iteration loop
After improving the skill:
1. Apply your improvements to the skill
2. Rerun all test cases into a new `iteration-<N+1>/` directory, including baseline runs. If you're creating a new skill, the baseline is always `without_skill` (no skill) — that stays the same across iterations. If you're improving an existing skill, use your judgment on what makes sense as the baseline: the original version the user came in with, or the previous iteration.
3. Launch the reviewer with `--previous-workspace` pointing at the previous iteration
4. Wait for the user to review and tell you they're done
5. Read the new feedback, improve again, repeat
Keep going until:
- The user says they're happy
- The feedback is all empty (everything looks good)
- You're not making meaningful progress
---
## Advanced: Blind comparison
For situations where you want a more rigorous comparison between two versions of a skill (e.g., the user asks "is the new version actually better?"), there's a blind comparison system. Read `agents/comparator.md` and `agents/analyzer.md` for the details. The basic idea is: give two outputs to an independent agent without telling it which is which, and let it judge quality. Then analyze why the winner won.
This is optional, requires subagents, and most users won't need it. The human review loop is usually sufficient.
---
## Description Optimization
The description field in SKILL.md frontmatter is the primary mechanism that determines whether Claude invokes a skill. After creating or improving a skill, offer to optimize the description for better triggering accuracy.
### Step 1: Generate trigger eval queries
Create 20 eval queries — a mix of should-trigger and should-not-trigger. Save as JSON:
```json
[
{"query": "the user prompt", "should_trigger": true},
{"query": "another prompt", "should_trigger": false}
]
```
The queries must be realistic and something a Claude Code or Claude.ai user would actually type. Not abstract requests, but requests that are concrete and specific and have a good amount of detail. For instance, file paths, personal context about the user's job or situation, column names and values, company names, URLs. A little bit of backstory. Some might be in lowercase or contain abbreviations or typos or casual speech. Use a mix of different lengths, and focus on edge cases rather than making them clear-cut (the user will get a chance to sign off on them).
Bad: `"Format this data"`, `"Extract text from PDF"`, `"Create a chart"`
Good: `"ok so my boss just sent me this xlsx file (its in my downloads, called something like 'Q4 sales final FINAL v2.xlsx') and she wants me to add a column that shows the profit margin as a percentage. The revenue is in column C and costs are in column D i think"`
For the **should-trigger** queries (8-10), think about coverage. You want different phrasings of the same intent — some formal, some casual. Include cases where the user doesn't explicitly name the skill or file type but clearly needs it. Throw in some uncommon use cases and cases where this skill competes with another but should win.
For the **should-not-trigger** queries (8-10), the most valuable ones are the near-misses — queries that share keywords or concepts with the skill but actually need something different. Think adjacent domains, ambiguous phrasing where a naive keyword match would trigger but shouldn't, and cases where the query touches on something the skill does but in a context where another tool is more appropriate.
The key thing to avoid: don't make should-not-trigger queries obviously irrelevant. "Write a fibonacci function" as a negative test for a PDF skill is too easy — it doesn't test anything. The negative cases should be genuinely tricky.
### Step 2: Review with user
Present the eval set to the user for review using the HTML template:
1. Read the template from `assets/eval_review.html`
2. Replace the placeholders:
- `__EVAL_DATA_PLACEHOLDER__` → the JSON array of eval items (no quotes around it — it's a JS variable assignment)
- `__SKILL_NAME_PLACEHOLDER__` → the skill's name
- `__SKILL_DESCRIPTION_PLACEHOLDER__` → the skill's current description
3. Write to a temp file (e.g., `/tmp/eval_review_<skill-name>.html`) and open it: `open /tmp/eval_review_<skill-name>.html`
4. The user can edit queries, toggle should-trigger, add/remove entries, then click "Export Eval Set"
5. The file downloads to `~/Downloads/eval_set.json` — check the Downloads folder for the most recent version in case there are multiple (e.g., `eval_set (1).json`)
This step matters — bad eval queries lead to bad descriptions.
### Step 3: Run the optimization loop
Tell the user: "This will take some time — I'll run the optimization loop in the background and check on it periodically."
Save the eval set to the workspace, then run in the background:
```bash
python -m scripts.run_loop \
--eval-set <path-to-trigger-eval.json> \
--skill-path <path-to-skill> \
--model <model-id-powering-this-session> \
--max-iterations 5 \
--verbose
```
Use the model ID from your system prompt (the one powering the current session) so the triggering test matches what the user actually experiences.
While it runs, periodically tail the output to give the user updates on which iteration it's on and what the scores look like.
This handles the full optimization loop automatically. It splits the eval set into 60% train and 40% held-out test, evaluates the current description (running each query 3 times to get a reliable trigger rate), then calls Claude to propose improvements based on what failed. It re-evaluates each new description on both train and test, iterating up to 5 times. When it's done, it opens an HTML report in the browser showing the results per iteration and returns JSON with `best_description` — selected by test score rather than train score to avoid overfitting.
### How skill triggering works
Understanding the triggering mechanism helps design better eval queries. Skills appear in Claude's `available_skills` list with their name + description, and Claude decides whether to consult a skill based on that description. The important thing to know is that Claude only consults skills for tasks it can't easily handle on its own — simple, one-step queries like "read this PDF" may not trigger a skill even if the description matches perfectly, because Claude can handle them directly with basic tools. Complex, multi-step, or specialized queries reliably trigger skills when the description matches.
This means your eval queries should be substantive enough that Claude would actually benefit from consulting a skill. Simple queries like "read file X" are poor test cases — they won't trigger skills regardless of description quality.
### Step 4: Apply the result
Take `best_description` from the JSON output and update the skill's SKILL.md frontmatter. Show the user before/after and report the scores.
---
### Package and Present (only if `present_files` tool is available)
Check whether you have access to the `present_files` tool. If you don't, skip this step. If you do, package the skill and present the .skill file to the user:
```bash
python -m scripts.package_skill <path/to/skill-folder>
```
After packaging, direct the user to the resulting `.skill` file path so they can install it.
---
## Claude.ai-specific instructions
In Claude.ai, the core workflow is the same (draft → test → review → improve → repeat), but because Claude.ai doesn't have subagents, some mechanics change. Here's what to adapt:
**Running test cases**: No subagents means no parallel execution. For each test case, read the skill's SKILL.md, then follow its instructions to accomplish the test prompt yourself. Do them one at a time. This is less rigorous than independent subagents (you wrote the skill and you're also running it, so you have full context), but it's a useful sanity check — and the human review step compensates. Skip the baseline runs — just use the skill to complete the task as requested.
**Reviewing results**: If you can't open a browser (e.g., Claude.ai's VM has no display, or you're on a remote server), skip the browser reviewer entirely. Instead, present results directly in the conversation. For each test case, show the prompt and the output. If the output is a file the user needs to see (like a .docx or .xlsx), save it to the filesystem and tell them where it is so they can download and inspect it. Ask for feedback inline: "How does this look? Anything you'd change?"
**Benchmarking**: Skip the quantitative benchmarking — it relies on baseline comparisons which aren't meaningful without subagents. Focus on qualitative feedback from the user.
**The iteration loop**: Same as before — improve the skill, rerun the test cases, ask for feedback — just without the browser reviewer in the middle. You can still organize results into iteration directories on the filesystem if you have one.
**Description optimization**: This section requires the `claude` CLI tool (specifically `claude -p`) which is only available in Claude Code. Skip it if you're on Claude.ai.
**Blind comparison**: Requires subagents. Skip it.
**Packaging**: The `package_skill.py` script works anywhere with Python and a filesystem. On Claude.ai, you can run it and the user can download the resulting `.skill` file.
**Updating an existing skill**: The user might be asking you to update an existing skill, not create a new one. In this case:
- **Preserve the original name.** Note the skill's directory name and `name` frontmatter field -- use them unchanged. E.g., if the installed skill is `research-helper`, output `research-helper.skill` (not `research-helper-v2`).
- **Copy to a writeable location before editing.** The installed skill path may be read-only. Copy to `/tmp/skill-name/`, edit there, and package from the copy.
- **If packaging manually, stage in `/tmp/` first**, then copy to the output directory -- direct writes may fail due to permissions.
---
## Cowork-Specific Instructions
If you're in Cowork, the main things to know are:
- You have subagents, so the main workflow (spawn test cases in parallel, run baselines, grade, etc.) all works. (However, if you run into severe problems with timeouts, it's OK to run the test prompts in series rather than parallel.)
- You don't have a browser or display, so when generating the eval viewer, use `--static <output_path>` to write a standalone HTML file instead of starting a server. Then proffer a link that the user can click to open the HTML in their browser.
- For whatever reason, the Cowork setup seems to disincline Claude from generating the eval viewer after running the tests, so just to reiterate: whether you're in Cowork or in Claude Code, after running tests, you should always generate the eval viewer for the human to look at examples before revising the skill yourself and trying to make corrections, using `generate_review.py` (not writing your own boutique html code). Sorry in advance but I'm gonna go all caps here: GENERATE THE EVAL VIEWER *BEFORE* evaluating inputs yourself. You want to get them in front of the human ASAP!
- Feedback works differently: since there's no running server, the viewer's "Submit All Reviews" button will download `feedback.json` as a file. You can then read it from there (you may have to request access first).
- Packaging works — `package_skill.py` just needs Python and a filesystem.
- Description optimization (`run_loop.py` / `run_eval.py`) should work in Cowork just fine since it uses `claude -p` via subprocess, not a browser, but please save it until you've fully finished making the skill and the user agrees it's in good shape.
- **Updating an existing skill**: The user might be asking you to update an existing skill, not create a new one. Follow the update guidance in the claude.ai section above.
---
## Reference files
The agents/ directory contains instructions for specialized subagents. Read them when you need to spawn the relevant subagent.
- `agents/grader.md` — How to evaluate assertions against outputs
- `agents/comparator.md` — How to do blind A/B comparison between two outputs
- `agents/analyzer.md` — How to analyze why one version beat another
The references/ directory has additional documentation:
- `references/schemas.md` — JSON structures for evals.json, grading.json, etc.
---
Repeating one more time the core loop here for emphasis:
- Figure out what the skill is about
- Draft or edit the skill
- Run claude-with-access-to-the-skill on test prompts
- With the user, evaluate the outputs:
- Create benchmark.json and run `eval-viewer/generate_review.py` to help the user review them
- Run quantitative evals
- Repeat until you and the user are satisfied
- Package the final skill and return it to the user.
Please add steps to your TodoList, if you have such a thing, to make sure you don't forget. If you're in Cowork, please specifically put "Create evals JSON and run `eval-viewer/generate_review.py` so human can review test cases" in your TodoList to make sure it happens.
Good luck!
skills/skill-creator/agents/analyzer.md
+274
View File
@@ -0,0 +1,274 @@
# Post-hoc Analyzer Agent
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
## Role
After the blind comparator determines a winner, the Post-hoc Analyzer "unblids" the results by examining the skills and transcripts. The goal is to extract actionable insights: what made the winner better, and how can the loser be improved?
## Inputs
You receive these parameters in your prompt:
- **winner**: "A" or "B" (from blind comparison)
- **winner_skill_path**: Path to the skill that produced the winning output
- **winner_transcript_path**: Path to the execution transcript for the winner
- **loser_skill_path**: Path to the skill that produced the losing output
- **loser_transcript_path**: Path to the execution transcript for the loser
- **comparison_result_path**: Path to the blind comparator's output JSON
- **output_path**: Where to save the analysis results
## Process
### Step 1: Read Comparison Result
1. Read the blind comparator's output at comparison_result_path
2. Note the winning side (A or B), the reasoning, and any scores
3. Understand what the comparator valued in the winning output
### Step 2: Read Both Skills
1. Read the winner skill's SKILL.md and key referenced files
2. Read the loser skill's SKILL.md and key referenced files
3. Identify structural differences:
- Instructions clarity and specificity
- Script/tool usage patterns
- Example coverage
- Edge case handling
### Step 3: Read Both Transcripts
1. Read the winner's transcript
2. Read the loser's transcript
3. Compare execution patterns:
- How closely did each follow their skill's instructions?
- What tools were used differently?
- Where did the loser diverge from optimal behavior?
- Did either encounter errors or make recovery attempts?
### Step 4: Analyze Instruction Following
For each transcript, evaluate:
- Did the agent follow the skill's explicit instructions?
- Did the agent use the skill's provided tools/scripts?
- Were there missed opportunities to leverage skill content?
- Did the agent add unnecessary steps not in the skill?
Score instruction following 1-10 and note specific issues.
### Step 5: Identify Winner Strengths
Determine what made the winner better:
- Clearer instructions that led to better behavior?
- Better scripts/tools that produced better output?
- More comprehensive examples that guided edge cases?
- Better error handling guidance?
Be specific. Quote from skills/transcripts where relevant.
### Step 6: Identify Loser Weaknesses
Determine what held the loser back:
- Ambiguous instructions that led to suboptimal choices?
- Missing tools/scripts that forced workarounds?
- Gaps in edge case coverage?
- Poor error handling that caused failures?
### Step 7: Generate Improvement Suggestions
Based on the analysis, produce actionable suggestions for improving the loser skill:
- Specific instruction changes to make
- Tools/scripts to add or modify
- Examples to include
- Edge cases to address
Prioritize by impact. Focus on changes that would have changed the outcome.
### Step 8: Write Analysis Results
Save structured analysis to `{output_path}`.
## Output Format
Write a JSON file with this structure:
```json
{
"comparison_summary": {
"winner": "A",
"winner_skill": "path/to/winner/skill",
"loser_skill": "path/to/loser/skill",
"comparator_reasoning": "Brief summary of why comparator chose winner"
},
"winner_strengths": [
"Clear step-by-step instructions for handling multi-page documents",
"Included validation script that caught formatting errors",
"Explicit guidance on fallback behavior when OCR fails"
],
"loser_weaknesses": [
"Vague instruction 'process the document appropriately' led to inconsistent behavior",
"No script for validation, agent had to improvise and made errors",
"No guidance on OCR failure, agent gave up instead of trying alternatives"
],
"instruction_following": {
"winner": {
"score": 9,
"issues": [
"Minor: skipped optional logging step"
]
},
"loser": {
"score": 6,
"issues": [
"Did not use the skill's formatting template",
"Invented own approach instead of following step 3",
"Missed the 'always validate output' instruction"
]
}
},
"improvement_suggestions": [
{
"priority": "high",
"category": "instructions",
"suggestion": "Replace 'process the document appropriately' with explicit steps: 1) Extract text, 2) Identify sections, 3) Format per template",
"expected_impact": "Would eliminate ambiguity that caused inconsistent behavior"
},
{
"priority": "high",
"category": "tools",
"suggestion": "Add validate_output.py script similar to winner skill's validation approach",
"expected_impact": "Would catch formatting errors before final output"
},
{
"priority": "medium",
"category": "error_handling",
"suggestion": "Add fallback instructions: 'If OCR fails, try: 1) different resolution, 2) image preprocessing, 3) manual extraction'",
"expected_impact": "Would prevent early failure on difficult documents"
}
],
"transcript_insights": {
"winner_execution_pattern": "Read skill -> Followed 5-step process -> Used validation script -> Fixed 2 issues -> Produced output",
"loser_execution_pattern": "Read skill -> Unclear on approach -> Tried 3 different methods -> No validation -> Output had errors"
}
}
```
## Guidelines
- **Be specific**: Quote from skills and transcripts, don't just say "instructions were unclear"
- **Be actionable**: Suggestions should be concrete changes, not vague advice
- **Focus on skill improvements**: The goal is to improve the losing skill, not critique the agent
- **Prioritize by impact**: Which changes would most likely have changed the outcome?
- **Consider causation**: Did the skill weakness actually cause the worse output, or is it incidental?
- **Stay objective**: Analyze what happened, don't editorialize
- **Think about generalization**: Would this improvement help on other evals too?
## Categories for Suggestions
Use these categories to organize improvement suggestions:
| Category | Description |
|----------|-------------|
| `instructions` | Changes to the skill's prose instructions |
| `tools` | Scripts, templates, or utilities to add/modify |
| `examples` | Example inputs/outputs to include |
| `error_handling` | Guidance for handling failures |
| `structure` | Reorganization of skill content |
| `references` | External docs or resources to add |
## Priority Levels
- **high**: Would likely change the outcome of this comparison
- **medium**: Would improve quality but may not change win/loss
- **low**: Nice to have, marginal improvement
---
# Analyzing Benchmark Results
When analyzing benchmark results, the analyzer's purpose is to **surface patterns and anomalies** across multiple runs, not suggest skill improvements.
## Role
Review all benchmark run results and generate freeform notes that help the user understand skill performance. Focus on patterns that wouldn't be visible from aggregate metrics alone.
## Inputs
You receive these parameters in your prompt:
- **benchmark_data_path**: Path to the in-progress benchmark.json with all run results
- **skill_path**: Path to the skill being benchmarked
- **output_path**: Where to save the notes (as JSON array of strings)
## Process
### Step 1: Read Benchmark Data
1. Read the benchmark.json containing all run results
2. Note the configurations tested (with_skill, without_skill)
3. Understand the run_summary aggregates already calculated
### Step 2: Analyze Per-Assertion Patterns
For each expectation across all runs:
- Does it **always pass** in both configurations? (may not differentiate skill value)
- Does it **always fail** in both configurations? (may be broken or beyond capability)
- Does it **always pass with skill but fail without**? (skill clearly adds value here)
- Does it **always fail with skill but pass without**? (skill may be hurting)
- Is it **highly variable**? (flaky expectation or non-deterministic behavior)
### Step 3: Analyze Cross-Eval Patterns
Look for patterns across evals:
- Are certain eval types consistently harder/easier?
- Do some evals show high variance while others are stable?
- Are there surprising results that contradict expectations?
### Step 4: Analyze Metrics Patterns
Look at time_seconds, tokens, tool_calls:
- Does the skill significantly increase execution time?
- Is there high variance in resource usage?
- Are there outlier runs that skew the aggregates?
### Step 5: Generate Notes
Write freeform observations as a list of strings. Each note should:
- State a specific observation
- Be grounded in the data (not speculation)
- Help the user understand something the aggregate metrics don't show
Examples:
- "Assertion 'Output is a PDF file' passes 100% in both configurations - may not differentiate skill value"
- "Eval 3 shows high variance (50% ± 40%) - run 2 had an unusual failure that may be flaky"
- "Without-skill runs consistently fail on table extraction expectations (0% pass rate)"
- "Skill adds 13s average execution time but improves pass rate by 50%"
- "Token usage is 80% higher with skill, primarily due to script output parsing"
- "All 3 without-skill runs for eval 1 produced empty output"
### Step 6: Write Notes
Save notes to `{output_path}` as a JSON array of strings:
```json
[
"Assertion 'Output is a PDF file' passes 100% in both configurations - may not differentiate skill value",
"Eval 3 shows high variance (50% ± 40%) - run 2 had an unusual failure",
"Without-skill runs consistently fail on table extraction expectations",
"Skill adds 13s average execution time but improves pass rate by 50%"
]
```
## Guidelines
**DO:**
- Report what you observe in the data
- Be specific about which evals, expectations, or runs you're referring to
- Note patterns that aggregate metrics would hide
- Provide context that helps interpret the numbers
**DO NOT:**
- Suggest improvements to the skill (that's for the improvement step, not benchmarking)
- Make subjective quality judgments ("the output was good/bad")
- Speculate about causes without evidence
- Repeat information already in the run_summary aggregates
skills/skill-creator/agents/comparator.md
+202
View File
@@ -0,0 +1,202 @@
# Blind Comparator Agent
Compare two outputs WITHOUT knowing which skill produced them.
## Role
The Blind Comparator judges which output better accomplishes the eval task. You receive two outputs labeled A and B, but you do NOT know which skill produced which. This prevents bias toward a particular skill or approach.
Your judgment is based purely on output quality and task completion.
## Inputs
You receive these parameters in your prompt:
- **output_a_path**: Path to the first output file or directory
- **output_b_path**: Path to the second output file or directory
- **eval_prompt**: The original task/prompt that was executed
- **expectations**: List of expectations to check (optional - may be empty)
## Process
### Step 1: Read Both Outputs
1. Examine output A (file or directory)
2. Examine output B (file or directory)
3. Note the type, structure, and content of each
4. If outputs are directories, examine all relevant files inside
### Step 2: Understand the Task
1. Read the eval_prompt carefully
2. Identify what the task requires:
- What should be produced?
- What qualities matter (accuracy, completeness, format)?
- What would distinguish a good output from a poor one?
### Step 3: Generate Evaluation Rubric
Based on the task, generate a rubric with two dimensions:
**Content Rubric** (what the output contains):
| Criterion | 1 (Poor) | 3 (Acceptable) | 5 (Excellent) |
|-----------|----------|----------------|---------------|
| Correctness | Major errors | Minor errors | Fully correct |
| Completeness | Missing key elements | Mostly complete | All elements present |
| Accuracy | Significant inaccuracies | Minor inaccuracies | Accurate throughout |
**Structure Rubric** (how the output is organized):
| Criterion | 1 (Poor) | 3 (Acceptable) | 5 (Excellent) |
|-----------|----------|----------------|---------------|
| Organization | Disorganized | Reasonably organized | Clear, logical structure |
| Formatting | Inconsistent/broken | Mostly consistent | Professional, polished |
| Usability | Difficult to use | Usable with effort | Easy to use |
Adapt criteria to the specific task. For example:
- PDF form → "Field alignment", "Text readability", "Data placement"
- Document → "Section structure", "Heading hierarchy", "Paragraph flow"
- Data output → "Schema correctness", "Data types", "Completeness"
### Step 4: Evaluate Each Output Against the Rubric
For each output (A and B):
1. **Score each criterion** on the rubric (1-5 scale)
2. **Calculate dimension totals**: Content score, Structure score
3. **Calculate overall score**: Average of dimension scores, scaled to 1-10
### Step 5: Check Assertions (if provided)
If expectations are provided:
1. Check each expectation against output A
2. Check each expectation against output B
3. Count pass rates for each output
4. Use expectation scores as secondary evidence (not the primary decision factor)
### Step 6: Determine the Winner
Compare A and B based on (in priority order):
1. **Primary**: Overall rubric score (content + structure)
2. **Secondary**: Assertion pass rates (if applicable)
3. **Tiebreaker**: If truly equal, declare a TIE
Be decisive - ties should be rare. One output is usually better, even if marginally.
### Step 7: Write Comparison Results
Save results to a JSON file at the path specified (or `comparison.json` if not specified).
## Output Format
Write a JSON file with this structure:
```json
{
"winner": "A",
"reasoning": "Output A provides a complete solution with proper formatting and all required fields. Output B is missing the date field and has formatting inconsistencies.",
"rubric": {
"A": {
"content": {
"correctness": 5,
"completeness": 5,
"accuracy": 4
},
"structure": {
"organization": 4,
"formatting": 5,
"usability": 4
},
"content_score": 4.7,
"structure_score": 4.3,
"overall_score": 9.0
},
"B": {
"content": {
"correctness": 3,
"completeness": 2,
"accuracy": 3
},
"structure": {
"organization": 3,
"formatting": 2,
"usability": 3
},
"content_score": 2.7,
"structure_score": 2.7,
"overall_score": 5.4
}
},
"output_quality": {
"A": {
"score": 9,
"strengths": ["Complete solution", "Well-formatted", "All fields present"],
"weaknesses": ["Minor style inconsistency in header"]
},
"B": {
"score": 5,
"strengths": ["Readable output", "Correct basic structure"],
"weaknesses": ["Missing date field", "Formatting inconsistencies", "Partial data extraction"]
}
},
"expectation_results": {
"A": {
"passed": 4,
"total": 5,
"pass_rate": 0.80,
"details": [
{"text": "Output includes name", "passed": true},
{"text": "Output includes date", "passed": true},
{"text": "Format is PDF", "passed": true},
{"text": "Contains signature", "passed": false},
{"text": "Readable text", "passed": true}
]
},
"B": {
"passed": 3,
"total": 5,
"pass_rate": 0.60,
"details": [
{"text": "Output includes name", "passed": true},
{"text": "Output includes date", "passed": false},
{"text": "Format is PDF", "passed": true},
{"text": "Contains signature", "passed": false},
{"text": "Readable text", "passed": true}
]
}
}
}
```
If no expectations were provided, omit the `expectation_results` field entirely.
## Field Descriptions
- **winner**: "A", "B", or "TIE"
- **reasoning**: Clear explanation of why the winner was chosen (or why it's a tie)
- **rubric**: Structured rubric evaluation for each output
- **content**: Scores for content criteria (correctness, completeness, accuracy)
- **structure**: Scores for structure criteria (organization, formatting, usability)
- **content_score**: Average of content criteria (1-5)
- **structure_score**: Average of structure criteria (1-5)
- **overall_score**: Combined score scaled to 1-10
- **output_quality**: Summary quality assessment
- **score**: 1-10 rating (should match rubric overall_score)
- **strengths**: List of positive aspects
- **weaknesses**: List of issues or shortcomings
- **expectation_results**: (Only if expectations provided)
- **passed**: Number of expectations that passed
- **total**: Total number of expectations
- **pass_rate**: Fraction passed (0.0 to 1.0)
- **details**: Individual expectation results
## Guidelines
- **Stay blind**: DO NOT try to infer which skill produced which output. Judge purely on output quality.
- **Be specific**: Cite specific examples when explaining strengths and weaknesses.
- **Be decisive**: Choose a winner unless outputs are genuinely equivalent.
- **Output quality first**: Assertion scores are secondary to overall task completion.
- **Be objective**: Don't favor outputs based on style preferences; focus on correctness and completeness.
- **Explain your reasoning**: The reasoning field should make it clear why you chose the winner.
- **Handle edge cases**: If both outputs fail, pick the one that fails less badly. If both are excellent, pick the one that's marginally better.
skills/skill-creator/agents/grader.md
+223
View File
@@ -0,0 +1,223 @@
# Grader Agent
Evaluate expectations against an execution transcript and outputs.
## Role
The Grader reviews a transcript and output files, then determines whether each expectation passes or fails. Provide clear evidence for each judgment.
You have two jobs: grade the outputs, and critique the evals themselves. A passing grade on a weak assertion is worse than useless — it creates false confidence. When you notice an assertion that's trivially satisfied, or an important outcome that no assertion checks, say so.
## Inputs
You receive these parameters in your prompt:
- **expectations**: List of expectations to evaluate (strings)
- **transcript_path**: Path to the execution transcript (markdown file)
- **outputs_dir**: Directory containing output files from execution
## Process
### Step 1: Read the Transcript
1. Read the transcript file completely
2. Note the eval prompt, execution steps, and final result
3. Identify any issues or errors documented
### Step 2: Examine Output Files
1. List files in outputs_dir
2. Read/examine each file relevant to the expectations. If outputs aren't plain text, use the inspection tools provided in your prompt — don't rely solely on what the transcript says the executor produced.
3. Note contents, structure, and quality
### Step 3: Evaluate Each Assertion
For each expectation:
1. **Search for evidence** in the transcript and outputs
2. **Determine verdict**:
- **PASS**: Clear evidence the expectation is true AND the evidence reflects genuine task completion, not just surface-level compliance
- **FAIL**: No evidence, or evidence contradicts the expectation, or the evidence is superficial (e.g., correct filename but empty/wrong content)
3. **Cite the evidence**: Quote the specific text or describe what you found
### Step 4: Extract and Verify Claims
Beyond the predefined expectations, extract implicit claims from the outputs and verify them:
1. **Extract claims** from the transcript and outputs:
- Factual statements ("The form has 12 fields")
- Process claims ("Used pypdf to fill the form")
- Quality claims ("All fields were filled correctly")
2. **Verify each claim**:
- **Factual claims**: Can be checked against the outputs or external sources
- **Process claims**: Can be verified from the transcript
- **Quality claims**: Evaluate whether the claim is justified
3. **Flag unverifiable claims**: Note claims that cannot be verified with available information
This catches issues that predefined expectations might miss.
### Step 5: Read User Notes
If `{outputs_dir}/user_notes.md` exists:
1. Read it and note any uncertainties or issues flagged by the executor
2. Include relevant concerns in the grading output
3. These may reveal problems even when expectations pass
### Step 6: Critique the Evals
After grading, consider whether the evals themselves could be improved. Only surface suggestions when there's a clear gap.
Good suggestions test meaningful outcomes — assertions that are hard to satisfy without actually doing the work correctly. Think about what makes an assertion *discriminating*: it passes when the skill genuinely succeeds and fails when it doesn't.
Suggestions worth raising:
- An assertion that passed but would also pass for a clearly wrong output (e.g., checking filename existence but not file content)
- An important outcome you observed — good or bad — that no assertion covers at all
- An assertion that can't actually be verified from the available outputs
Keep the bar high. The goal is to flag things the eval author would say "good catch" about, not to nitpick every assertion.
### Step 7: Write Grading Results
Save results to `{outputs_dir}/../grading.json` (sibling to outputs_dir).
## Grading Criteria
**PASS when**:
- The transcript or outputs clearly demonstrate the expectation is true
- Specific evidence can be cited
- The evidence reflects genuine substance, not just surface compliance (e.g., a file exists AND contains correct content, not just the right filename)
**FAIL when**:
- No evidence found for the expectation
- Evidence contradicts the expectation
- The expectation cannot be verified from available information
- The evidence is superficial — the assertion is technically satisfied but the underlying task outcome is wrong or incomplete
- The output appears to meet the assertion by coincidence rather than by actually doing the work
**When uncertain**: The burden of proof to pass is on the expectation.
### Step 8: Read Executor Metrics and Timing
1. If `{outputs_dir}/metrics.json` exists, read it and include in grading output
2. If `{outputs_dir}/../timing.json` exists, read it and include timing data
## Output Format
Write a JSON file with this structure:
```json
{
"expectations": [
{
"text": "The output includes the name 'John Smith'",
"passed": true,
"evidence": "Found in transcript Step 3: 'Extracted names: John Smith, Sarah Johnson'"
},
{
"text": "The spreadsheet has a SUM formula in cell B10",
"passed": false,
"evidence": "No spreadsheet was created. The output was a text file."
},
{
"text": "The assistant used the skill's OCR script",
"passed": true,
"evidence": "Transcript Step 2 shows: 'Tool: Bash - python ocr_script.py image.png'"
}
],
"summary": {
"passed": 2,
"failed": 1,
"total": 3,
"pass_rate": 0.67
},
"execution_metrics": {
"tool_calls": {
"Read": 5,
"Write": 2,
"Bash": 8
},
"total_tool_calls": 15,
"total_steps": 6,
"errors_encountered": 0,
"output_chars": 12450,
"transcript_chars": 3200
},
"timing": {
"executor_duration_seconds": 165.0,
"grader_duration_seconds": 26.0,
"total_duration_seconds": 191.0
},
"claims": [
{
"claim": "The form has 12 fillable fields",
"type": "factual",
"verified": true,
"evidence": "Counted 12 fields in field_info.json"
},
{
"claim": "All required fields were populated",
"type": "quality",
"verified": false,
"evidence": "Reference section was left blank despite data being available"
}
],
"user_notes_summary": {
"uncertainties": ["Used 2023 data, may be stale"],
"needs_review": [],
"workarounds": ["Fell back to text overlay for non-fillable fields"]
},
"eval_feedback": {
"suggestions": [
{
"assertion": "The output includes the name 'John Smith'",
"reason": "A hallucinated document that mentions the name would also pass — consider checking it appears as the primary contact with matching phone and email from the input"
},
{
"reason": "No assertion checks whether the extracted phone numbers match the input — I observed incorrect numbers in the output that went uncaught"
}
],
"overall": "Assertions check presence but not correctness. Consider adding content verification."
}
}
```
## Field Descriptions
- **expectations**: Array of graded expectations
- **text**: The original expectation text
- **passed**: Boolean - true if expectation passes
- **evidence**: Specific quote or description supporting the verdict
- **summary**: Aggregate statistics
- **passed**: Count of passed expectations
- **failed**: Count of failed expectations
- **total**: Total expectations evaluated
- **pass_rate**: Fraction passed (0.0 to 1.0)
- **execution_metrics**: Copied from executor's metrics.json (if available)
- **output_chars**: Total character count of output files (proxy for tokens)
- **transcript_chars**: Character count of transcript
- **timing**: Wall clock timing from timing.json (if available)
- **executor_duration_seconds**: Time spent in executor subagent
- **total_duration_seconds**: Total elapsed time for the run
- **claims**: Extracted and verified claims from the output
- **claim**: The statement being verified
- **type**: "factual", "process", or "quality"
- **verified**: Boolean - whether the claim holds
- **evidence**: Supporting or contradicting evidence
- **user_notes_summary**: Issues flagged by the executor
- **uncertainties**: Things the executor wasn't sure about
- **needs_review**: Items requiring human attention
- **workarounds**: Places where the skill didn't work as expected
- **eval_feedback**: Improvement suggestions for the evals (only when warranted)
- **suggestions**: List of concrete suggestions, each with a `reason` and optionally an `assertion` it relates to
- **overall**: Brief assessment — can be "No suggestions, evals look solid" if nothing to flag
## Guidelines
- **Be objective**: Base verdicts on evidence, not assumptions
- **Be specific**: Quote the exact text that supports your verdict
- **Be thorough**: Check both transcript and output files
- **Be consistent**: Apply the same standard to each expectation
- **Explain failures**: Make it clear why evidence was insufficient
- **No partial credit**: Each expectation is pass or fail, not partial
skills/skill-creator/assets/eval_review.html
+146
View File
@@ -0,0 +1,146 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Eval Set Review - __SKILL_NAME_PLACEHOLDER__</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Poppins:wght@500;600&family=Lora:wght@400;500&display=swap" rel="stylesheet">
<style>
* { box-sizing: border-box; margin: 0; padding: 0; }
body { font-family: 'Lora', Georgia, serif; background: #faf9f5; padding: 2rem; color: #141413; }
h1 { font-family: 'Poppins', sans-serif; margin-bottom: 0.5rem; font-size: 1.5rem; }
.description { color: #b0aea5; margin-bottom: 1.5rem; font-style: italic; max-width: 900px; }
.controls { margin-bottom: 1rem; display: flex; gap: 0.5rem; }
.btn { font-family: 'Poppins', sans-serif; padding: 0.5rem 1rem; border: none; border-radius: 6px; cursor: pointer; font-size: 0.875rem; font-weight: 500; }
.btn-add { background: #6a9bcc; color: white; }
.btn-add:hover { background: #5889b8; }
.btn-export { background: #d97757; color: white; }
.btn-export:hover { background: #c4613f; }
table { width: 100%; max-width: 1100px; border-collapse: collapse; background: white; border-radius: 6px; overflow: hidden; box-shadow: 0 1px 3px rgba(0,0,0,0.08); }
th { font-family: 'Poppins', sans-serif; background: #141413; color: #faf9f5; padding: 0.75rem 1rem; text-align: left; font-size: 0.875rem; }
td { padding: 0.75rem 1rem; border-bottom: 1px solid #e8e6dc; vertical-align: top; }
tr:nth-child(even) td { background: #faf9f5; }
tr:hover td { background: #f3f1ea; }
.section-header td { background: #e8e6dc; font-family: 'Poppins', sans-serif; font-weight: 500; font-size: 0.8rem; color: #141413; text-transform: uppercase; letter-spacing: 0.05em; }
.query-input { width: 100%; padding: 0.4rem; border: 1px solid #e8e6dc; border-radius: 4px; font-size: 0.875rem; font-family: 'Lora', Georgia, serif; resize: vertical; min-height: 60px; }
.query-input:focus { outline: none; border-color: #d97757; box-shadow: 0 0 0 2px rgba(217,119,87,0.15); }
.toggle { position: relative; display: inline-block; width: 44px; height: 24px; }
.toggle input { opacity: 0; width: 0; height: 0; }
.toggle .slider { position: absolute; inset: 0; background: #b0aea5; border-radius: 24px; cursor: pointer; transition: 0.2s; }
.toggle .slider::before { content: ""; position: absolute; width: 18px; height: 18px; left: 3px; bottom: 3px; background: white; border-radius: 50%; transition: 0.2s; }
.toggle input:checked + .slider { background: #d97757; }
.toggle input:checked + .slider::before { transform: translateX(20px); }
.btn-delete { background: #c44; color: white; padding: 0.3rem 0.6rem; border: none; border-radius: 4px; cursor: pointer; font-size: 0.75rem; font-family: 'Poppins', sans-serif; }
.btn-delete:hover { background: #a33; }
.summary { margin-top: 1rem; color: #b0aea5; font-size: 0.875rem; }
</style>
</head>
<body>
<h1>Eval Set Review: <span id="skill-name">__SKILL_NAME_PLACEHOLDER__</span></h1>
<p class="description">Current description: <span id="skill-desc">__SKILL_DESCRIPTION_PLACEHOLDER__</span></p>
<div class="controls">
<button class="btn btn-add" onclick="addRow()">+ Add Query</button>
<button class="btn btn-export" onclick="exportEvalSet()">Export Eval Set</button>
</div>
<table>
<thead>
<tr>
<th style="width:65%">Query</th>
<th style="width:18%">Should Trigger</th>
<th style="width:10%">Actions</th>
</tr>
</thead>
<tbody id="eval-body"></tbody>
</table>
<p class="summary" id="summary"></p>
<script>
const EVAL_DATA = __EVAL_DATA_PLACEHOLDER__;
let evalItems = [...EVAL_DATA];
function render() {
const tbody = document.getElementById('eval-body');
tbody.innerHTML = '';
// Sort: should-trigger first, then should-not-trigger
const sorted = evalItems
.map((item, origIdx) => ({ ...item, origIdx }))
.sort((a, b) => (b.should_trigger ? 1 : 0) - (a.should_trigger ? 1 : 0));
let lastGroup = null;
sorted.forEach(item => {
const group = item.should_trigger ? 'trigger' : 'no-trigger';
if (group !== lastGroup) {
const headerRow = document.createElement('tr');
headerRow.className = 'section-header';
headerRow.innerHTML = `<td colspan="3">${item.should_trigger ? 'Should Trigger' : 'Should NOT Trigger'}</td>`;
tbody.appendChild(headerRow);
lastGroup = group;
}
const idx = item.origIdx;
const tr = document.createElement('tr');
tr.innerHTML = `
<td><textarea class="query-input" onchange="updateQuery(${idx}, this.value)">${escapeHtml(item.query)}</textarea></td>
<td>
<label class="toggle">
<input type="checkbox" ${item.should_trigger ? 'checked' : ''} onchange="updateTrigger(${idx}, this.checked)">
<span class="slider"></span>
</label>
<span style="margin-left:8px;font-size:0.8rem;color:#b0aea5">${item.should_trigger ? 'Yes' : 'No'}</span>
</td>
<td><button class="btn-delete" onclick="deleteRow(${idx})">Delete</button></td>
`;
tbody.appendChild(tr);
});
updateSummary();
}
function escapeHtml(text) {
const div = document.createElement('div');
div.textContent = text;
return div.innerHTML;
}
function updateQuery(idx, value) { evalItems[idx].query = value; updateSummary(); }
function updateTrigger(idx, value) { evalItems[idx].should_trigger = value; render(); }
function deleteRow(idx) { evalItems.splice(idx, 1); render(); }
function addRow() {
evalItems.push({ query: '', should_trigger: true });
render();
const inputs = document.querySelectorAll('.query-input');
inputs[inputs.length - 1].focus();
}
function updateSummary() {
const trigger = evalItems.filter(i => i.should_trigger).length;
const noTrigger = evalItems.filter(i => !i.should_trigger).length;
document.getElementById('summary').textContent =
`${evalItems.length} queries total: ${trigger} should trigger, ${noTrigger} should not trigger`;
}
function exportEvalSet() {
const valid = evalItems.filter(i => i.query.trim() !== '');
const data = valid.map(i => ({ query: i.query.trim(), should_trigger: i.should_trigger }));
const blob = new Blob([JSON.stringify(data, null, 2)], { type: 'application/json' });
const url = URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url;
a.download = 'eval_set.json';
document.body.appendChild(a);
a.click();
document.body.removeChild(a);
URL.revokeObjectURL(url);
}
render();
</script>
</body>
</html>
skills/skill-creator/eval-viewer/generate_review.py
+471
View File
@@ -0,0 +1,471 @@
#!/usr/bin/env python3
"""Generate and serve a review page for eval results.
Reads the workspace directory, discovers runs (directories with outputs/),
embeds all output data into a self-contained HTML page, and serves it via
a tiny HTTP server. Feedback auto-saves to feedback.json in the workspace.
Usage:
python generate_review.py <workspace-path> [--port PORT] [--skill-name NAME]
python generate_review.py <workspace-path> --previous-feedback /path/to/old/feedback.json
No dependencies beyond the Python stdlib are required.
"""
import argparse
import base64
import json
import mimetypes
import os
import re
import signal
import subprocess
import sys
import time
import webbrowser
from functools import partial
from http.server import HTTPServer, BaseHTTPRequestHandler
from pathlib import Path
# Files to exclude from output listings
METADATA_FILES = {"transcript.md", "user_notes.md", "metrics.json"}
# Extensions we render as inline text
TEXT_EXTENSIONS = {
".txt", ".md", ".json", ".csv", ".py", ".js", ".ts", ".tsx", ".jsx",
".yaml", ".yml", ".xml", ".html", ".css", ".sh", ".rb", ".go", ".rs",
".java", ".c", ".cpp", ".h", ".hpp", ".sql", ".r", ".toml",
}
# Extensions we render as inline images
IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".gif", ".svg", ".webp"}
# MIME type overrides for common types
MIME_OVERRIDES = {
".svg": "image/svg+xml",
".xlsx": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
".docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
".pptx": "application/vnd.openxmlformats-officedocument.presentationml.presentation",
}
def get_mime_type(path: Path) -> str:
ext = path.suffix.lower()
if ext in MIME_OVERRIDES:
return MIME_OVERRIDES[ext]
mime, _ = mimetypes.guess_type(str(path))
return mime or "application/octet-stream"
def find_runs(workspace: Path) -> list[dict]:
"""Recursively find directories that contain an outputs/ subdirectory."""
runs: list[dict] = []
_find_runs_recursive(workspace, workspace, runs)
runs.sort(key=lambda r: (r.get("eval_id", float("inf")), r["id"]))
return runs
def _find_runs_recursive(root: Path, current: Path, runs: list[dict]) -> None:
if not current.is_dir():
return
outputs_dir = current / "outputs"
if outputs_dir.is_dir():
run = build_run(root, current)
if run:
runs.append(run)
return
skip = {"node_modules", ".git", "__pycache__", "skill", "inputs"}
for child in sorted(current.iterdir()):
if child.is_dir() and child.name not in skip:
_find_runs_recursive(root, child, runs)
def build_run(root: Path, run_dir: Path) -> dict | None:
"""Build a run dict with prompt, outputs, and grading data."""
prompt = ""
eval_id = None
# Try eval_metadata.json
for candidate in [run_dir / "eval_metadata.json", run_dir.parent / "eval_metadata.json"]:
if candidate.exists():
try:
metadata = json.loads(candidate.read_text())
prompt = metadata.get("prompt", "")
eval_id = metadata.get("eval_id")
except (json.JSONDecodeError, OSError):
pass
if prompt:
break
# Fall back to transcript.md
if not prompt:
for candidate in [run_dir / "transcript.md", run_dir / "outputs" / "transcript.md"]:
if candidate.exists():
try:
text = candidate.read_text()
match = re.search(r"## Eval Prompt\n\n([\s\S]*?)(?=\n##|$)", text)
if match:
prompt = match.group(1).strip()
except OSError:
pass
if prompt:
break
if not prompt:
prompt = "(No prompt found)"
run_id = str(run_dir.relative_to(root)).replace("/", "-").replace("\\", "-")
# Collect output files
outputs_dir = run_dir / "outputs"
output_files: list[dict] = []
if outputs_dir.is_dir():
for f in sorted(outputs_dir.iterdir()):
if f.is_file() and f.name not in METADATA_FILES:
output_files.append(embed_file(f))
# Load grading if present
grading = None
for candidate in [run_dir / "grading.json", run_dir.parent / "grading.json"]:
if candidate.exists():
try:
grading = json.loads(candidate.read_text())
except (json.JSONDecodeError, OSError):
pass
if grading:
break
return {
"id": run_id,
"prompt": prompt,
"eval_id": eval_id,
"outputs": output_files,
"grading": grading,
}
def embed_file(path: Path) -> dict:
"""Read a file and return an embedded representation."""
ext = path.suffix.lower()
mime = get_mime_type(path)
if ext in TEXT_EXTENSIONS:
try:
content = path.read_text(errors="replace")
except OSError:
content = "(Error reading file)"
return {
"name": path.name,
"type": "text",
"content": content,
}
elif ext in IMAGE_EXTENSIONS:
try:
raw = path.read_bytes()
b64 = base64.b64encode(raw).decode("ascii")
except OSError:
return {"name": path.name, "type": "error", "content": "(Error reading file)"}
return {
"name": path.name,
"type": "image",
"mime": mime,
"data_uri": f"data:{mime};base64,{b64}",
}
elif ext == ".pdf":
try:
raw = path.read_bytes()
b64 = base64.b64encode(raw).decode("ascii")
except OSError:
return {"name": path.name, "type": "error", "content": "(Error reading file)"}
return {
"name": path.name,
"type": "pdf",
"data_uri": f"data:{mime};base64,{b64}",
}
elif ext == ".xlsx":
try:
raw = path.read_bytes()
b64 = base64.b64encode(raw).decode("ascii")
except OSError:
return {"name": path.name, "type": "error", "content": "(Error reading file)"}
return {
"name": path.name,
"type": "xlsx",
"data_b64": b64,
}
else:
# Binary / unknown — base64 download link
try:
raw = path.read_bytes()
b64 = base64.b64encode(raw).decode("ascii")
except OSError:
return {"name": path.name, "type": "error", "content": "(Error reading file)"}
return {
"name": path.name,
"type": "binary",
"mime": mime,
"data_uri": f"data:{mime};base64,{b64}",
}
def load_previous_iteration(workspace: Path) -> dict[str, dict]:
"""Load previous iteration's feedback and outputs.
Returns a map of run_id -> {"feedback": str, "outputs": list[dict]}.
"""
result: dict[str, dict] = {}
# Load feedback
feedback_map: dict[str, str] = {}
feedback_path = workspace / "feedback.json"
if feedback_path.exists():
try:
data = json.loads(feedback_path.read_text())
feedback_map = {
r["run_id"]: r["feedback"]
for r in data.get("reviews", [])
if r.get("feedback", "").strip()
}
except (json.JSONDecodeError, OSError, KeyError):
pass
# Load runs (to get outputs)
prev_runs = find_runs(workspace)
for run in prev_runs:
result[run["id"]] = {
"feedback": feedback_map.get(run["id"], ""),
"outputs": run.get("outputs", []),
}
# Also add feedback for run_ids that had feedback but no matching run
for run_id, fb in feedback_map.items():
if run_id not in result:
result[run_id] = {"feedback": fb, "outputs": []}
return result
def generate_html(
runs: list[dict],
skill_name: str,
previous: dict[str, dict] | None = None,
benchmark: dict | None = None,
) -> str:
"""Generate the complete standalone HTML page with embedded data."""
template_path = Path(__file__).parent / "viewer.html"
template = template_path.read_text()
# Build previous_feedback and previous_outputs maps for the template
previous_feedback: dict[str, str] = {}
previous_outputs: dict[str, list[dict]] = {}
if previous:
for run_id, data in previous.items():
if data.get("feedback"):
previous_feedback[run_id] = data["feedback"]
if data.get("outputs"):
previous_outputs[run_id] = data["outputs"]
embedded = {
"skill_name": skill_name,
"runs": runs,
"previous_feedback": previous_feedback,
"previous_outputs": previous_outputs,
}
if benchmark:
embedded["benchmark"] = benchmark
data_json = json.dumps(embedded)
return template.replace("/*__EMBEDDED_DATA__*/", f"const EMBEDDED_DATA = {data_json};")
# ---------------------------------------------------------------------------
# HTTP server (stdlib only, zero dependencies)
# ---------------------------------------------------------------------------
def _kill_port(port: int) -> None:
"""Kill any process listening on the given port."""
try:
result = subprocess.run(
["lsof", "-ti", f":{port}"],
capture_output=True, text=True, timeout=5,
)
for pid_str in result.stdout.strip().split("\n"):
if pid_str.strip():
try:
os.kill(int(pid_str.strip()), signal.SIGTERM)
except (ProcessLookupError, ValueError):
pass
if result.stdout.strip():
time.sleep(0.5)
except subprocess.TimeoutExpired:
pass
except FileNotFoundError:
print("Note: lsof not found, cannot check if port is in use", file=sys.stderr)
class ReviewHandler(BaseHTTPRequestHandler):
"""Serves the review HTML and handles feedback saves.
Regenerates the HTML on each page load so that refreshing the browser
picks up new eval outputs without restarting the server.
"""
def __init__(
self,
workspace: Path,
skill_name: str,
feedback_path: Path,
previous: dict[str, dict],
benchmark_path: Path | None,
*args,
**kwargs,
):
self.workspace = workspace
self.skill_name = skill_name
self.feedback_path = feedback_path
self.previous = previous
self.benchmark_path = benchmark_path
super().__init__(*args, **kwargs)
def do_GET(self) -> None:
if self.path == "/" or self.path == "/index.html":
# Regenerate HTML on each request (re-scans workspace for new outputs)
runs = find_runs(self.workspace)
benchmark = None
if self.benchmark_path and self.benchmark_path.exists():
try:
benchmark = json.loads(self.benchmark_path.read_text())
except (json.JSONDecodeError, OSError):
pass
html = generate_html(runs, self.skill_name, self.previous, benchmark)
content = html.encode("utf-8")
self.send_response(200)
self.send_header("Content-Type", "text/html; charset=utf-8")
self.send_header("Content-Length", str(len(content)))
self.end_headers()
self.wfile.write(content)
elif self.path == "/api/feedback":
data = b"{}"
if self.feedback_path.exists():
data = self.feedback_path.read_bytes()
self.send_response(200)
self.send_header("Content-Type", "application/json")
self.send_header("Content-Length", str(len(data)))
self.end_headers()
self.wfile.write(data)
else:
self.send_error(404)
def do_POST(self) -> None:
if self.path == "/api/feedback":
length = int(self.headers.get("Content-Length", 0))
body = self.rfile.read(length)
try:
data = json.loads(body)
if not isinstance(data, dict) or "reviews" not in data:
raise ValueError("Expected JSON object with 'reviews' key")
self.feedback_path.write_text(json.dumps(data, indent=2) + "\n")
resp = b'{"ok":true}'
self.send_response(200)
except (json.JSONDecodeError, OSError, ValueError) as e:
resp = json.dumps({"error": str(e)}).encode()
self.send_response(500)
self.send_header("Content-Type", "application/json")
self.send_header("Content-Length", str(len(resp)))
self.end_headers()
self.wfile.write(resp)
else:
self.send_error(404)
def log_message(self, format: str, *args: object) -> None:
# Suppress request logging to keep terminal clean
pass
def main() -> None:
parser = argparse.ArgumentParser(description="Generate and serve eval review")
parser.add_argument("workspace", type=Path, help="Path to workspace directory")
parser.add_argument("--port", "-p", type=int, default=3117, help="Server port (default: 3117)")
parser.add_argument("--skill-name", "-n", type=str, default=None, help="Skill name for header")
parser.add_argument(
"--previous-workspace", type=Path, default=None,
help="Path to previous iteration's workspace (shows old outputs and feedback as context)",
)
parser.add_argument(
"--benchmark", type=Path, default=None,
help="Path to benchmark.json to show in the Benchmark tab",
)
parser.add_argument(
"--static", "-s", type=Path, default=None,
help="Write standalone HTML to this path instead of starting a server",
)
args = parser.parse_args()
workspace = args.workspace.resolve()
if not workspace.is_dir():
print(f"Error: {workspace} is not a directory", file=sys.stderr)
sys.exit(1)
runs = find_runs(workspace)
if not runs:
print(f"No runs found in {workspace}", file=sys.stderr)
sys.exit(1)
skill_name = args.skill_name or workspace.name.replace("-workspace", "")
feedback_path = workspace / "feedback.json"
previous: dict[str, dict] = {}
if args.previous_workspace:
previous = load_previous_iteration(args.previous_workspace.resolve())
benchmark_path = args.benchmark.resolve() if args.benchmark else None
benchmark = None
if benchmark_path and benchmark_path.exists():
try:
benchmark = json.loads(benchmark_path.read_text())
except (json.JSONDecodeError, OSError):
pass
if args.static:
html = generate_html(runs, skill_name, previous, benchmark)
args.static.parent.mkdir(parents=True, exist_ok=True)
args.static.write_text(html)
print(f"\n Static viewer written to: {args.static}\n")
sys.exit(0)
# Kill any existing process on the target port
port = args.port
_kill_port(port)
handler = partial(ReviewHandler, workspace, skill_name, feedback_path, previous, benchmark_path)
try:
server = HTTPServer(("127.0.0.1", port), handler)
except OSError:
# Port still in use after kill attempt — find a free one
server = HTTPServer(("127.0.0.1", 0), handler)
port = server.server_address[1]
url = f"http://localhost:{port}"
print(f"\n Eval Viewer")
print(f" ─────────────────────────────────")
print(f" URL: {url}")
print(f" Workspace: {workspace}")
print(f" Feedback: {feedback_path}")
if previous:
print(f" Previous: {args.previous_workspace} ({len(previous)} runs)")
if benchmark_path:
print(f" Benchmark: {benchmark_path}")
print(f"\n Press Ctrl+C to stop.\n")
webbrowser.open(url)
try:
server.serve_forever()
except KeyboardInterrupt:
print("\nStopped.")
server.server_close()
if __name__ == "__main__":
main()
skills/skill-creator/eval-viewer/viewer.html
+1325
View File
File diff suppressed because it is too large Load Diff
skills/skill-creator/references/schemas.md
+430
View File
@@ -0,0 +1,430 @@
# JSON Schemas
This document defines the JSON schemas used by skill-creator.
---
## evals.json
Defines the evals for a skill. Located at `evals/evals.json` within the skill directory.
```json
{
"skill_name": "example-skill",
"evals": [
{
"id": 1,
"prompt": "User's example prompt",
"expected_output": "Description of expected result",
"files": ["evals/files/sample1.pdf"],
"expectations": [
"The output includes X",
"The skill used script Y"
]
}
]
}
```
**Fields:**
- `skill_name`: Name matching the skill's frontmatter
- `evals[].id`: Unique integer identifier
- `evals[].prompt`: The task to execute
- `evals[].expected_output`: Human-readable description of success
- `evals[].files`: Optional list of input file paths (relative to skill root)
- `evals[].expectations`: List of verifiable statements
---
## history.json
Tracks version progression in Improve mode. Located at workspace root.
```json
{
"started_at": "2026-01-15T10:30:00Z",
"skill_name": "pdf",
"current_best": "v2",
"iterations": [
{
"version": "v0",
"parent": null,
"expectation_pass_rate": 0.65,
"grading_result": "baseline",
"is_current_best": false
},
{
"version": "v1",
"parent": "v0",
"expectation_pass_rate": 0.75,
"grading_result": "won",
"is_current_best": false
},
{
"version": "v2",
"parent": "v1",
"expectation_pass_rate": 0.85,
"grading_result": "won",
"is_current_best": true
}
]
}
```
**Fields:**
- `started_at`: ISO timestamp of when improvement started
- `skill_name`: Name of the skill being improved
- `current_best`: Version identifier of the best performer
- `iterations[].version`: Version identifier (v0, v1, ...)
- `iterations[].parent`: Parent version this was derived from
- `iterations[].expectation_pass_rate`: Pass rate from grading
- `iterations[].grading_result`: "baseline", "won", "lost", or "tie"
- `iterations[].is_current_best`: Whether this is the current best version
---
## grading.json
Output from the grader agent. Located at `<run-dir>/grading.json`.
```json
{
"expectations": [
{
"text": "The output includes the name 'John Smith'",
"passed": true,
"evidence": "Found in transcript Step 3: 'Extracted names: John Smith, Sarah Johnson'"
},
{
"text": "The spreadsheet has a SUM formula in cell B10",
"passed": false,
"evidence": "No spreadsheet was created. The output was a text file."
}
],
"summary": {
"passed": 2,
"failed": 1,
"total": 3,
"pass_rate": 0.67
},
"execution_metrics": {
"tool_calls": {
"Read": 5,
"Write": 2,
"Bash": 8
},
"total_tool_calls": 15,
"total_steps": 6,
"errors_encountered": 0,
"output_chars": 12450,
"transcript_chars": 3200
},
"timing": {
"executor_duration_seconds": 165.0,
"grader_duration_seconds": 26.0,
"total_duration_seconds": 191.0
},
"claims": [
{
"claim": "The form has 12 fillable fields",
"type": "factual",
"verified": true,
"evidence": "Counted 12 fields in field_info.json"
}
],
"user_notes_summary": {
"uncertainties": ["Used 2023 data, may be stale"],
"needs_review": [],
"workarounds": ["Fell back to text overlay for non-fillable fields"]
},
"eval_feedback": {
"suggestions": [
{
"assertion": "The output includes the name 'John Smith'",
"reason": "A hallucinated document that mentions the name would also pass"
}
],
"overall": "Assertions check presence but not correctness."
}
}
```
**Fields:**
- `expectations[]`: Graded expectations with evidence
- `summary`: Aggregate pass/fail counts
- `execution_metrics`: Tool usage and output size (from executor's metrics.json)
- `timing`: Wall clock timing (from timing.json)
- `claims`: Extracted and verified claims from the output
- `user_notes_summary`: Issues flagged by the executor
- `eval_feedback`: (optional) Improvement suggestions for the evals, only present when the grader identifies issues worth raising
---
## metrics.json
Output from the executor agent. Located at `<run-dir>/outputs/metrics.json`.
```json
{
"tool_calls": {
"Read": 5,
"Write": 2,
"Bash": 8,
"Edit": 1,
"Glob": 2,
"Grep": 0
},
"total_tool_calls": 18,
"total_steps": 6,
"files_created": ["filled_form.pdf", "field_values.json"],
"errors_encountered": 0,
"output_chars": 12450,
"transcript_chars": 3200
}
```
**Fields:**
- `tool_calls`: Count per tool type
- `total_tool_calls`: Sum of all tool calls
- `total_steps`: Number of major execution steps
- `files_created`: List of output files created
- `errors_encountered`: Number of errors during execution
- `output_chars`: Total character count of output files
- `transcript_chars`: Character count of transcript
---
## timing.json
Wall clock timing for a run. Located at `<run-dir>/timing.json`.
**How to capture:** When a subagent task completes, the task notification includes `total_tokens` and `duration_ms`. Save these immediately — they are not persisted anywhere else and cannot be recovered after the fact.
```json
{
"total_tokens": 84852,
"duration_ms": 23332,
"total_duration_seconds": 23.3,
"executor_start": "2026-01-15T10:30:00Z",
"executor_end": "2026-01-15T10:32:45Z",
"executor_duration_seconds": 165.0,
"grader_start": "2026-01-15T10:32:46Z",
"grader_end": "2026-01-15T10:33:12Z",
"grader_duration_seconds": 26.0
}
```
---
## benchmark.json
Output from Benchmark mode. Located at `benchmarks/<timestamp>/benchmark.json`.
```json
{
"metadata": {
"skill_name": "pdf",
"skill_path": "/path/to/pdf",
"executor_model": "claude-sonnet-4-20250514",
"analyzer_model": "most-capable-model",
"timestamp": "2026-01-15T10:30:00Z",
"evals_run": [1, 2, 3],
"runs_per_configuration": 3
},
"runs": [
{
"eval_id": 1,
"eval_name": "Ocean",
"configuration": "with_skill",
"run_number": 1,
"result": {
"pass_rate": 0.85,
"passed": 6,
"failed": 1,
"total": 7,
"time_seconds": 42.5,
"tokens": 3800,
"tool_calls": 18,
"errors": 0
},
"expectations": [
{"text": "...", "passed": true, "evidence": "..."}
],
"notes": [
"Used 2023 data, may be stale",
"Fell back to text overlay for non-fillable fields"
]
}
],
"run_summary": {
"with_skill": {
"pass_rate": {"mean": 0.85, "stddev": 0.05, "min": 0.80, "max": 0.90},
"time_seconds": {"mean": 45.0, "stddev": 12.0, "min": 32.0, "max": 58.0},
"tokens": {"mean": 3800, "stddev": 400, "min": 3200, "max": 4100}
},
"without_skill": {
"pass_rate": {"mean": 0.35, "stddev": 0.08, "min": 0.28, "max": 0.45},
"time_seconds": {"mean": 32.0, "stddev": 8.0, "min": 24.0, "max": 42.0},
"tokens": {"mean": 2100, "stddev": 300, "min": 1800, "max": 2500}
},
"delta": {
"pass_rate": "+0.50",
"time_seconds": "+13.0",
"tokens": "+1700"
}
},
"notes": [
"Assertion 'Output is a PDF file' passes 100% in both configurations - may not differentiate skill value",
"Eval 3 shows high variance (50% ± 40%) - may be flaky or model-dependent",
"Without-skill runs consistently fail on table extraction expectations",
"Skill adds 13s average execution time but improves pass rate by 50%"
]
}
```
**Fields:**
- `metadata`: Information about the benchmark run
- `skill_name`: Name of the skill
- `timestamp`: When the benchmark was run
- `evals_run`: List of eval names or IDs
- `runs_per_configuration`: Number of runs per config (e.g. 3)
- `runs[]`: Individual run results
- `eval_id`: Numeric eval identifier
- `eval_name`: Human-readable eval name (used as section header in the viewer)
- `configuration`: Must be `"with_skill"` or `"without_skill"` (the viewer uses this exact string for grouping and color coding)
- `run_number`: Integer run number (1, 2, 3...)
- `result`: Nested object with `pass_rate`, `passed`, `total`, `time_seconds`, `tokens`, `errors`
- `run_summary`: Statistical aggregates per configuration
- `with_skill` / `without_skill`: Each contains `pass_rate`, `time_seconds`, `tokens` objects with `mean` and `stddev` fields
- `delta`: Difference strings like `"+0.50"`, `"+13.0"`, `"+1700"`
- `notes`: Freeform observations from the analyzer
**Important:** The viewer reads these field names exactly. Using `config` instead of `configuration`, or putting `pass_rate` at the top level of a run instead of nested under `result`, will cause the viewer to show empty/zero values. Always reference this schema when generating benchmark.json manually.
---
## comparison.json
Output from blind comparator. Located at `<grading-dir>/comparison-N.json`.
```json
{
"winner": "A",
"reasoning": "Output A provides a complete solution with proper formatting and all required fields. Output B is missing the date field and has formatting inconsistencies.",
"rubric": {
"A": {
"content": {
"correctness": 5,
"completeness": 5,
"accuracy": 4
},
"structure": {
"organization": 4,
"formatting": 5,
"usability": 4
},
"content_score": 4.7,
"structure_score": 4.3,
"overall_score": 9.0
},
"B": {
"content": {
"correctness": 3,
"completeness": 2,
"accuracy": 3
},
"structure": {
"organization": 3,
"formatting": 2,
"usability": 3
},
"content_score": 2.7,
"structure_score": 2.7,
"overall_score": 5.4
}
},
"output_quality": {
"A": {
"score": 9,
"strengths": ["Complete solution", "Well-formatted", "All fields present"],
"weaknesses": ["Minor style inconsistency in header"]
},
"B": {
"score": 5,
"strengths": ["Readable output", "Correct basic structure"],
"weaknesses": ["Missing date field", "Formatting inconsistencies", "Partial data extraction"]
}
},
"expectation_results": {
"A": {
"passed": 4,
"total": 5,
"pass_rate": 0.80,
"details": [
{"text": "Output includes name", "passed": true}
]
},
"B": {
"passed": 3,
"total": 5,
"pass_rate": 0.60,
"details": [
{"text": "Output includes name", "passed": true}
]
}
}
}
```
---
## analysis.json
Output from post-hoc analyzer. Located at `<grading-dir>/analysis.json`.
```json
{
"comparison_summary": {
"winner": "A",
"winner_skill": "path/to/winner/skill",
"loser_skill": "path/to/loser/skill",
"comparator_reasoning": "Brief summary of why comparator chose winner"
},
"winner_strengths": [
"Clear step-by-step instructions for handling multi-page documents",
"Included validation script that caught formatting errors"
],
"loser_weaknesses": [
"Vague instruction 'process the document appropriately' led to inconsistent behavior",
"No script for validation, agent had to improvise"
],
"instruction_following": {
"winner": {
"score": 9,
"issues": ["Minor: skipped optional logging step"]
},
"loser": {
"score": 6,
"issues": [
"Did not use the skill's formatting template",
"Invented own approach instead of following step 3"
]
}
},
"improvement_suggestions": [
{
"priority": "high",
"category": "instructions",
"suggestion": "Replace 'process the document appropriately' with explicit steps",
"expected_impact": "Would eliminate ambiguity that caused inconsistent behavior"
}
],
"transcript_insights": {
"winner_execution_pattern": "Read skill -> Followed 5-step process -> Used validation script",
"loser_execution_pattern": "Read skill -> Unclear on approach -> Tried 3 different methods"
}
}
```
skills/skill-creator/scripts/__init__.py
View File
skills/skill-creator/scripts/aggregate_benchmark.py
+401
View File
@@ -0,0 +1,401 @@
#!/usr/bin/env python3
"""
Aggregate individual run results into benchmark summary statistics.
Reads grading.json files from run directories and produces:
- run_summary with mean, stddev, min, max for each metric
- delta between with_skill and without_skill configurations
Usage:
python aggregate_benchmark.py <benchmark_dir>
Example:
python aggregate_benchmark.py benchmarks/2026-01-15T10-30-00/
The script supports two directory layouts:
Workspace layout (from skill-creator iterations):
<benchmark_dir>/
└── eval-N/
├── with_skill/
│ ├── run-1/grading.json
│ └── run-2/grading.json
└── without_skill/
├── run-1/grading.json
└── run-2/grading.json
Legacy layout (with runs/ subdirectory):
<benchmark_dir>/
└── runs/
└── eval-N/
├── with_skill/
│ └── run-1/grading.json
└── without_skill/
└── run-1/grading.json
"""
import argparse
import json
import math
import sys
from datetime import datetime, timezone
from pathlib import Path
def calculate_stats(values: list[float]) -> dict:
"""Calculate mean, stddev, min, max for a list of values."""
if not values:
return {"mean": 0.0, "stddev": 0.0, "min": 0.0, "max": 0.0}
n = len(values)
mean = sum(values) / n
if n > 1:
variance = sum((x - mean) ** 2 for x in values) / (n - 1)
stddev = math.sqrt(variance)
else:
stddev = 0.0
return {
"mean": round(mean, 4),
"stddev": round(stddev, 4),
"min": round(min(values), 4),
"max": round(max(values), 4)
}
def load_run_results(benchmark_dir: Path) -> dict:
"""
Load all run results from a benchmark directory.
Returns dict keyed by config name (e.g. "with_skill"/"without_skill",
or "new_skill"/"old_skill"), each containing a list of run results.
"""
# Support both layouts: eval dirs directly under benchmark_dir, or under runs/
runs_dir = benchmark_dir / "runs"
if runs_dir.exists():
search_dir = runs_dir
elif list(benchmark_dir.glob("eval-*")):
search_dir = benchmark_dir
else:
print(f"No eval directories found in {benchmark_dir} or {benchmark_dir / 'runs'}")
return {}
results: dict[str, list] = {}
for eval_idx, eval_dir in enumerate(sorted(search_dir.glob("eval-*"))):
metadata_path = eval_dir / "eval_metadata.json"
if metadata_path.exists():
try:
with open(metadata_path) as mf:
eval_id = json.load(mf).get("eval_id", eval_idx)
except (json.JSONDecodeError, OSError):
eval_id = eval_idx
else:
try:
eval_id = int(eval_dir.name.split("-")[1])
except ValueError:
eval_id = eval_idx
# Discover config directories dynamically rather than hardcoding names
for config_dir in sorted(eval_dir.iterdir()):
if not config_dir.is_dir():
continue
# Skip non-config directories (inputs, outputs, etc.)
if not list(config_dir.glob("run-*")):
continue
config = config_dir.name
if config not in results:
results[config] = []
for run_dir in sorted(config_dir.glob("run-*")):
run_number = int(run_dir.name.split("-")[1])
grading_file = run_dir / "grading.json"
if not grading_file.exists():
print(f"Warning: grading.json not found in {run_dir}")
continue
try:
with open(grading_file) as f:
grading = json.load(f)
except json.JSONDecodeError as e:
print(f"Warning: Invalid JSON in {grading_file}: {e}")
continue
# Extract metrics
result = {
"eval_id": eval_id,
"run_number": run_number,
"pass_rate": grading.get("summary", {}).get("pass_rate", 0.0),
"passed": grading.get("summary", {}).get("passed", 0),
"failed": grading.get("summary", {}).get("failed", 0),
"total": grading.get("summary", {}).get("total", 0),
}
# Extract timing — check grading.json first, then sibling timing.json
timing = grading.get("timing", {})
result["time_seconds"] = timing.get("total_duration_seconds", 0.0)
timing_file = run_dir / "timing.json"
if result["time_seconds"] == 0.0 and timing_file.exists():
try:
with open(timing_file) as tf:
timing_data = json.load(tf)
result["time_seconds"] = timing_data.get("total_duration_seconds", 0.0)
result["tokens"] = timing_data.get("total_tokens", 0)
except json.JSONDecodeError:
pass
# Extract metrics if available
metrics = grading.get("execution_metrics", {})
result["tool_calls"] = metrics.get("total_tool_calls", 0)
if not result.get("tokens"):
result["tokens"] = metrics.get("output_chars", 0)
result["errors"] = metrics.get("errors_encountered", 0)
# Extract expectations — viewer requires fields: text, passed, evidence
raw_expectations = grading.get("expectations", [])
for exp in raw_expectations:
if "text" not in exp or "passed" not in exp:
print(f"Warning: expectation in {grading_file} missing required fields (text, passed, evidence): {exp}")
result["expectations"] = raw_expectations
# Extract notes from user_notes_summary
notes_summary = grading.get("user_notes_summary", {})
notes = []
notes.extend(notes_summary.get("uncertainties", []))
notes.extend(notes_summary.get("needs_review", []))
notes.extend(notes_summary.get("workarounds", []))
result["notes"] = notes
results[config].append(result)
return results
def aggregate_results(results: dict) -> dict:
"""
Aggregate run results into summary statistics.
Returns run_summary with stats for each configuration and delta.
"""
run_summary = {}
configs = list(results.keys())
for config in configs:
runs = results.get(config, [])
if not runs:
run_summary[config] = {
"pass_rate": {"mean": 0.0, "stddev": 0.0, "min": 0.0, "max": 0.0},
"time_seconds": {"mean": 0.0, "stddev": 0.0, "min": 0.0, "max": 0.0},
"tokens": {"mean": 0, "stddev": 0, "min": 0, "max": 0}
}
continue
pass_rates = [r["pass_rate"] for r in runs]
times = [r["time_seconds"] for r in runs]
tokens = [r.get("tokens", 0) for r in runs]
run_summary[config] = {
"pass_rate": calculate_stats(pass_rates),
"time_seconds": calculate_stats(times),
"tokens": calculate_stats(tokens)
}
# Calculate delta between the first two configs (if two exist)
if len(configs) >= 2:
primary = run_summary.get(configs[0], {})
baseline = run_summary.get(configs[1], {})
else:
primary = run_summary.get(configs[0], {}) if configs else {}
baseline = {}
delta_pass_rate = primary.get("pass_rate", {}).get("mean", 0) - baseline.get("pass_rate", {}).get("mean", 0)
delta_time = primary.get("time_seconds", {}).get("mean", 0) - baseline.get("time_seconds", {}).get("mean", 0)
delta_tokens = primary.get("tokens", {}).get("mean", 0) - baseline.get("tokens", {}).get("mean", 0)
run_summary["delta"] = {
"pass_rate": f"{delta_pass_rate:+.2f}",
"time_seconds": f"{delta_time:+.1f}",
"tokens": f"{delta_tokens:+.0f}"
}
return run_summary
def generate_benchmark(benchmark_dir: Path, skill_name: str = "", skill_path: str = "") -> dict:
"""
Generate complete benchmark.json from run results.
"""
results = load_run_results(benchmark_dir)
run_summary = aggregate_results(results)
# Build runs array for benchmark.json
runs = []
for config in results:
for result in results[config]:
runs.append({
"eval_id": result["eval_id"],
"configuration": config,
"run_number": result["run_number"],
"result": {
"pass_rate": result["pass_rate"],
"passed": result["passed"],
"failed": result["failed"],
"total": result["total"],
"time_seconds": result["time_seconds"],
"tokens": result.get("tokens", 0),
"tool_calls": result.get("tool_calls", 0),
"errors": result.get("errors", 0)
},
"expectations": result["expectations"],
"notes": result["notes"]
})
# Determine eval IDs from results
eval_ids = sorted(set(
r["eval_id"]
for config in results.values()
for r in config
))
benchmark = {
"metadata": {
"skill_name": skill_name or "<skill-name>",
"skill_path": skill_path or "<path/to/skill>",
"executor_model": "<model-name>",
"analyzer_model": "<model-name>",
"timestamp": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
"evals_run": eval_ids,
"runs_per_configuration": 3
},
"runs": runs,
"run_summary": run_summary,
"notes": [] # To be filled by analyzer
}
return benchmark
def generate_markdown(benchmark: dict) -> str:
"""Generate human-readable benchmark.md from benchmark data."""
metadata = benchmark["metadata"]
run_summary = benchmark["run_summary"]
# Determine config names (excluding "delta")
configs = [k for k in run_summary if k != "delta"]
config_a = configs[0] if len(configs) >= 1 else "config_a"
config_b = configs[1] if len(configs) >= 2 else "config_b"
label_a = config_a.replace("_", " ").title()
label_b = config_b.replace("_", " ").title()
lines = [
f"# Skill Benchmark: {metadata['skill_name']}",
"",
f"**Model**: {metadata['executor_model']}",
f"**Date**: {metadata['timestamp']}",
f"**Evals**: {', '.join(map(str, metadata['evals_run']))} ({metadata['runs_per_configuration']} runs each per configuration)",
"",
"## Summary",
"",
f"| Metric | {label_a} | {label_b} | Delta |",
"|--------|------------|---------------|-------|",
]
a_summary = run_summary.get(config_a, {})
b_summary = run_summary.get(config_b, {})
delta = run_summary.get("delta", {})
# Format pass rate
a_pr = a_summary.get("pass_rate", {})
b_pr = b_summary.get("pass_rate", {})
lines.append(f"| Pass Rate | {a_pr.get('mean', 0)*100:.0f}% ± {a_pr.get('stddev', 0)*100:.0f}% | {b_pr.get('mean', 0)*100:.0f}% ± {b_pr.get('stddev', 0)*100:.0f}% | {delta.get('pass_rate', '—')} |")
# Format time
a_time = a_summary.get("time_seconds", {})
b_time = b_summary.get("time_seconds", {})
lines.append(f"| Time | {a_time.get('mean', 0):.1f}s ± {a_time.get('stddev', 0):.1f}s | {b_time.get('mean', 0):.1f}s ± {b_time.get('stddev', 0):.1f}s | {delta.get('time_seconds', '—')}s |")
# Format tokens
a_tokens = a_summary.get("tokens", {})
b_tokens = b_summary.get("tokens", {})
lines.append(f"| Tokens | {a_tokens.get('mean', 0):.0f} ± {a_tokens.get('stddev', 0):.0f} | {b_tokens.get('mean', 0):.0f} ± {b_tokens.get('stddev', 0):.0f} | {delta.get('tokens', '—')} |")
# Notes section
if benchmark.get("notes"):
lines.extend([
"",
"## Notes",
""
])
for note in benchmark["notes"]:
lines.append(f"- {note}")
return "\n".join(lines)
def main():
parser = argparse.ArgumentParser(
description="Aggregate benchmark run results into summary statistics"
)
parser.add_argument(
"benchmark_dir",
type=Path,
help="Path to the benchmark directory"
)
parser.add_argument(
"--skill-name",
default="",
help="Name of the skill being benchmarked"
)
parser.add_argument(
"--skill-path",
default="",
help="Path to the skill being benchmarked"
)
parser.add_argument(
"--output", "-o",
type=Path,
help="Output path for benchmark.json (default: <benchmark_dir>/benchmark.json)"
)
args = parser.parse_args()
if not args.benchmark_dir.exists():
print(f"Directory not found: {args.benchmark_dir}")
sys.exit(1)
# Generate benchmark
benchmark = generate_benchmark(args.benchmark_dir, args.skill_name, args.skill_path)
# Determine output paths
output_json = args.output or (args.benchmark_dir / "benchmark.json")
output_md = output_json.with_suffix(".md")
# Write benchmark.json
with open(output_json, "w") as f:
json.dump(benchmark, f, indent=2)
print(f"Generated: {output_json}")
# Write benchmark.md
markdown = generate_markdown(benchmark)
with open(output_md, "w") as f:
f.write(markdown)
print(f"Generated: {output_md}")
# Print summary
run_summary = benchmark["run_summary"]
configs = [k for k in run_summary if k != "delta"]
delta = run_summary.get("delta", {})
print(f"\nSummary:")
for config in configs:
pr = run_summary[config]["pass_rate"]["mean"]
label = config.replace("_", " ").title()
print(f" {label}: {pr*100:.1f}% pass rate")
print(f" Delta: {delta.get('pass_rate', '—')}")
if __name__ == "__main__":
main()
skills/skill-creator/scripts/generate_report.py
+326
View File
@@ -0,0 +1,326 @@
#!/usr/bin/env python3
"""Generate an HTML report from run_loop.py output.
Takes the JSON output from run_loop.py and generates a visual HTML report
showing each description attempt with check/x for each test case.
Distinguishes between train and test queries.
"""
import argparse
import html
import json
import sys
from pathlib import Path
def generate_html(data: dict, auto_refresh: bool = False, skill_name: str = "") -> str:
"""Generate HTML report from loop output data. If auto_refresh is True, adds a meta refresh tag."""
history = data.get("history", [])
holdout = data.get("holdout", 0)
title_prefix = html.escape(skill_name + " \u2014 ") if skill_name else ""
# Get all unique queries from train and test sets, with should_trigger info
train_queries: list[dict] = []
test_queries: list[dict] = []
if history:
for r in history[0].get("train_results", history[0].get("results", [])):
train_queries.append({"query": r["query"], "should_trigger": r.get("should_trigger", True)})
if history[0].get("test_results"):
for r in history[0].get("test_results", []):
test_queries.append({"query": r["query"], "should_trigger": r.get("should_trigger", True)})
refresh_tag = ' <meta http-equiv="refresh" content="5">\n' if auto_refresh else ""
html_parts = ["""<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
""" + refresh_tag + """ <title>""" + title_prefix + """Skill Description Optimization</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Poppins:wght@500;600&family=Lora:wght@400;500&display=swap" rel="stylesheet">
<style>
body {
font-family: 'Lora', Georgia, serif;
max-width: 100%;
margin: 0 auto;
padding: 20px;
background: #faf9f5;
color: #141413;
}
h1 { font-family: 'Poppins', sans-serif; color: #141413; }
.explainer {
background: white;
padding: 15px;
border-radius: 6px;
margin-bottom: 20px;
border: 1px solid #e8e6dc;
color: #b0aea5;
font-size: 0.875rem;
line-height: 1.6;
}
.summary {
background: white;
padding: 15px;
border-radius: 6px;
margin-bottom: 20px;
border: 1px solid #e8e6dc;
}
.summary p { margin: 5px 0; }
.best { color: #788c5d; font-weight: bold; }
.table-container {
overflow-x: auto;
width: 100%;
}
table {
border-collapse: collapse;
background: white;
border: 1px solid #e8e6dc;
border-radius: 6px;
font-size: 12px;
min-width: 100%;
}
th, td {
padding: 8px;
text-align: left;
border: 1px solid #e8e6dc;
white-space: normal;
word-wrap: break-word;
}
th {
font-family: 'Poppins', sans-serif;
background: #141413;
color: #faf9f5;
font-weight: 500;
}
th.test-col {
background: #6a9bcc;
}
th.query-col { min-width: 200px; }
td.description {
font-family: monospace;
font-size: 11px;
word-wrap: break-word;
max-width: 400px;
}
td.result {
text-align: center;
font-size: 16px;
min-width: 40px;
}
td.test-result {
background: #f0f6fc;
}
.pass { color: #788c5d; }
.fail { color: #c44; }
.rate {
font-size: 9px;
color: #b0aea5;
display: block;
}
tr:hover { background: #faf9f5; }
.score {
display: inline-block;
padding: 2px 6px;
border-radius: 4px;
font-weight: bold;
font-size: 11px;
}
.score-good { background: #eef2e8; color: #788c5d; }
.score-ok { background: #fef3c7; color: #d97706; }
.score-bad { background: #fceaea; color: #c44; }
.train-label { color: #b0aea5; font-size: 10px; }
.test-label { color: #6a9bcc; font-size: 10px; font-weight: bold; }
.best-row { background: #f5f8f2; }
th.positive-col { border-bottom: 3px solid #788c5d; }
th.negative-col { border-bottom: 3px solid #c44; }
th.test-col.positive-col { border-bottom: 3px solid #788c5d; }
th.test-col.negative-col { border-bottom: 3px solid #c44; }
.legend { font-family: 'Poppins', sans-serif; display: flex; gap: 20px; margin-bottom: 10px; font-size: 13px; align-items: center; }
.legend-item { display: flex; align-items: center; gap: 6px; }
.legend-swatch { width: 16px; height: 16px; border-radius: 3px; display: inline-block; }
.swatch-positive { background: #141413; border-bottom: 3px solid #788c5d; }
.swatch-negative { background: #141413; border-bottom: 3px solid #c44; }
.swatch-test { background: #6a9bcc; }
.swatch-train { background: #141413; }
</style>
</head>
<body>
<h1>""" + title_prefix + """Skill Description Optimization</h1>
<div class="explainer">
<strong>Optimizing your skill's description.</strong> This page updates automatically as Claude tests different versions of your skill's description. Each row is an iteration — a new description attempt. The columns show test queries: green checkmarks mean the skill triggered correctly (or correctly didn't trigger), red crosses mean it got it wrong. The "Train" score shows performance on queries used to improve the description; the "Test" score shows performance on held-out queries the optimizer hasn't seen. When it's done, Claude will apply the best-performing description to your skill.
</div>
"""]
# Summary section
best_test_score = data.get('best_test_score')
best_train_score = data.get('best_train_score')
html_parts.append(f"""
<div class="summary">
<p><strong>Original:</strong> {html.escape(data.get('original_description', 'N/A'))}</p>
<p class="best"><strong>Best:</strong> {html.escape(data.get('best_description', 'N/A'))}</p>
<p><strong>Best Score:</strong> {data.get('best_score', 'N/A')} {'(test)' if best_test_score else '(train)'}</p>
<p><strong>Iterations:</strong> {data.get('iterations_run', 0)} | <strong>Train:</strong> {data.get('train_size', '?')} | <strong>Test:</strong> {data.get('test_size', '?')}</p>
</div>
""")
# Legend
html_parts.append("""
<div class="legend">
<span style="font-weight:600">Query columns:</span>
<span class="legend-item"><span class="legend-swatch swatch-positive"></span> Should trigger</span>
<span class="legend-item"><span class="legend-swatch swatch-negative"></span> Should NOT trigger</span>
<span class="legend-item"><span class="legend-swatch swatch-train"></span> Train</span>
<span class="legend-item"><span class="legend-swatch swatch-test"></span> Test</span>
</div>
""")
# Table header
html_parts.append("""
<div class="table-container">
<table>
<thead>
<tr>
<th>Iter</th>
<th>Train</th>
<th>Test</th>
<th class="query-col">Description</th>
""")
# Add column headers for train queries
for qinfo in train_queries:
polarity = "positive-col" if qinfo["should_trigger"] else "negative-col"
html_parts.append(f' <th class="{polarity}">{html.escape(qinfo["query"])}</th>\n')
# Add column headers for test queries (different color)
for qinfo in test_queries:
polarity = "positive-col" if qinfo["should_trigger"] else "negative-col"
html_parts.append(f' <th class="test-col {polarity}">{html.escape(qinfo["query"])}</th>\n')
html_parts.append(""" </tr>
</thead>
<tbody>
""")
# Find best iteration for highlighting
if test_queries:
best_iter = max(history, key=lambda h: h.get("test_passed") or 0).get("iteration")
else:
best_iter = max(history, key=lambda h: h.get("train_passed", h.get("passed", 0))).get("iteration")
# Add rows for each iteration
for h in history:
iteration = h.get("iteration", "?")
train_passed = h.get("train_passed", h.get("passed", 0))
train_total = h.get("train_total", h.get("total", 0))
test_passed = h.get("test_passed")
test_total = h.get("test_total")
description = h.get("description", "")
train_results = h.get("train_results", h.get("results", []))
test_results = h.get("test_results", [])
# Create lookups for results by query
train_by_query = {r["query"]: r for r in train_results}
test_by_query = {r["query"]: r for r in test_results} if test_results else {}
# Compute aggregate correct/total runs across all retries
def aggregate_runs(results: list[dict]) -> tuple[int, int]:
correct = 0
total = 0
for r in results:
runs = r.get("runs", 0)
triggers = r.get("triggers", 0)
total += runs
if r.get("should_trigger", True):
correct += triggers
else:
correct += runs - triggers
return correct, total
train_correct, train_runs = aggregate_runs(train_results)
test_correct, test_runs = aggregate_runs(test_results)
# Determine score classes
def score_class(correct: int, total: int) -> str:
if total > 0:
ratio = correct / total
if ratio >= 0.8:
return "score-good"
elif ratio >= 0.5:
return "score-ok"
return "score-bad"
train_class = score_class(train_correct, train_runs)
test_class = score_class(test_correct, test_runs)
row_class = "best-row" if iteration == best_iter else ""
html_parts.append(f""" <tr class="{row_class}">
<td>{iteration}</td>
<td><span class="score {train_class}">{train_correct}/{train_runs}</span></td>
<td><span class="score {test_class}">{test_correct}/{test_runs}</span></td>
<td class="description">{html.escape(description)}</td>
""")
# Add result for each train query
for qinfo in train_queries:
r = train_by_query.get(qinfo["query"], {})
did_pass = r.get("pass", False)
triggers = r.get("triggers", 0)
runs = r.get("runs", 0)
icon = "✓" if did_pass else "✗"
css_class = "pass" if did_pass else "fail"
html_parts.append(f' <td class="result {css_class}">{icon}<span class="rate">{triggers}/{runs}</span></td>\n')
# Add result for each test query (with different background)
for qinfo in test_queries:
r = test_by_query.get(qinfo["query"], {})
did_pass = r.get("pass", False)
triggers = r.get("triggers", 0)
runs = r.get("runs", 0)
icon = "✓" if did_pass else "✗"
css_class = "pass" if did_pass else "fail"
html_parts.append(f' <td class="result test-result {css_class}">{icon}<span class="rate">{triggers}/{runs}</span></td>\n')
html_parts.append(" </tr>\n")
html_parts.append(""" </tbody>
</table>
</div>
""")
html_parts.append("""
</body>
</html>
""")
return "".join(html_parts)
def main():
parser = argparse.ArgumentParser(description="Generate HTML report from run_loop output")
parser.add_argument("input", help="Path to JSON output from run_loop.py (or - for stdin)")
parser.add_argument("-o", "--output", default=None, help="Output HTML file (default: stdout)")
parser.add_argument("--skill-name", default="", help="Skill name to include in the report title")
args = parser.parse_args()
if args.input == "-":
data = json.load(sys.stdin)
else:
data = json.loads(Path(args.input).read_text())
html_output = generate_html(data, skill_name=args.skill_name)
if args.output:
Path(args.output).write_text(html_output)
print(f"Report written to {args.output}", file=sys.stderr)
else:
print(html_output)
if __name__ == "__main__":
main()
skills/skill-creator/scripts/improve_description.py
+247
View File
@@ -0,0 +1,247 @@
#!/usr/bin/env python3
"""Improve a skill description based on eval results.
Takes eval results (from run_eval.py) and generates an improved description
by calling `claude -p` as a subprocess (same auth pattern as run_eval.py —
uses the session's Claude Code auth, no separate ANTHROPIC_API_KEY needed).
"""
import argparse
import json
import os
import re
import subprocess
import sys
from pathlib import Path
from scripts.utils import parse_skill_md
def _call_claude(prompt: str, model: str | None, timeout: int = 300) -> str:
"""Run `claude -p` with the prompt on stdin and return the text response.
Prompt goes over stdin (not argv) because it embeds the full SKILL.md
body and can easily exceed comfortable argv length.
"""
cmd = ["claude", "-p", "--output-format", "text"]
if model:
cmd.extend(["--model", model])
# Remove CLAUDECODE env var to allow nesting claude -p inside a
# Claude Code session. The guard is for interactive terminal conflicts;
# programmatic subprocess usage is safe. Same pattern as run_eval.py.
env = {k: v for k, v in os.environ.items() if k != "CLAUDECODE"}
result = subprocess.run(
cmd,
input=prompt,
capture_output=True,
text=True,
env=env,
timeout=timeout,
)
if result.returncode != 0:
raise RuntimeError(
f"claude -p exited {result.returncode}\nstderr: {result.stderr}"
)
return result.stdout
def improve_description(
skill_name: str,
skill_content: str,
current_description: str,
eval_results: dict,
history: list[dict],
model: str,
test_results: dict | None = None,
log_dir: Path | None = None,
iteration: int | None = None,
) -> str:
"""Call Claude to improve the description based on eval results."""
failed_triggers = [
r for r in eval_results["results"]
if r["should_trigger"] and not r["pass"]
]
false_triggers = [
r for r in eval_results["results"]
if not r["should_trigger"] and not r["pass"]
]
# Build scores summary
train_score = f"{eval_results['summary']['passed']}/{eval_results['summary']['total']}"
if test_results:
test_score = f"{test_results['summary']['passed']}/{test_results['summary']['total']}"
scores_summary = f"Train: {train_score}, Test: {test_score}"
else:
scores_summary = f"Train: {train_score}"
prompt = f"""You are optimizing a skill description for a Claude Code skill called "{skill_name}". A "skill" is sort of like a prompt, but with progressive disclosure -- there's a title and description that Claude sees when deciding whether to use the skill, and then if it does use the skill, it reads the .md file which has lots more details and potentially links to other resources in the skill folder like helper files and scripts and additional documentation or examples.
The description appears in Claude's "available_skills" list. When a user sends a query, Claude decides whether to invoke the skill based solely on the title and on this description. Your goal is to write a description that triggers for relevant queries, and doesn't trigger for irrelevant ones.
Here's the current description:
<current_description>
"{current_description}"
</current_description>
Current scores ({scores_summary}):
<scores_summary>
"""
if failed_triggers:
prompt += "FAILED TO TRIGGER (should have triggered but didn't):\n"
for r in failed_triggers:
prompt += f' - "{r["query"]}" (triggered {r["triggers"]}/{r["runs"]} times)\n'
prompt += "\n"
if false_triggers:
prompt += "FALSE TRIGGERS (triggered but shouldn't have):\n"
for r in false_triggers:
prompt += f' - "{r["query"]}" (triggered {r["triggers"]}/{r["runs"]} times)\n'
prompt += "\n"
if history:
prompt += "PREVIOUS ATTEMPTS (do NOT repeat these — try something structurally different):\n\n"
for h in history:
train_s = f"{h.get('train_passed', h.get('passed', 0))}/{h.get('train_total', h.get('total', 0))}"
test_s = f"{h.get('test_passed', '?')}/{h.get('test_total', '?')}" if h.get('test_passed') is not None else None
score_str = f"train={train_s}" + (f", test={test_s}" if test_s else "")
prompt += f'<attempt {score_str}>\n'
prompt += f'Description: "{h["description"]}"\n'
if "results" in h:
prompt += "Train results:\n"
for r in h["results"]:
status = "PASS" if r["pass"] else "FAIL"
prompt += f' [{status}] "{r["query"][:80]}" (triggered {r["triggers"]}/{r["runs"]})\n'
if h.get("note"):
prompt += f'Note: {h["note"]}\n'
prompt += "</attempt>\n\n"
prompt += f"""</scores_summary>
Skill content (for context on what the skill does):
<skill_content>
{skill_content}
</skill_content>
Based on the failures, write a new and improved description that is more likely to trigger correctly. When I say "based on the failures", it's a bit of a tricky line to walk because we don't want to overfit to the specific cases you're seeing. So what I DON'T want you to do is produce an ever-expanding list of specific queries that this skill should or shouldn't trigger for. Instead, try to generalize from the failures to broader categories of user intent and situations where this skill would be useful or not useful. The reason for this is twofold:
1. Avoid overfitting
2. The list might get loooong and it's injected into ALL queries and there might be a lot of skills, so we don't want to blow too much space on any given description.
Concretely, your description should not be more than about 100-200 words, even if that comes at the cost of accuracy. There is a hard limit of 1024 characters — descriptions over that will be truncated, so stay comfortably under it.
Here are some tips that we've found to work well in writing these descriptions:
- The skill should be phrased in the imperative -- "Use this skill for" rather than "this skill does"
- The skill description should focus on the user's intent, what they are trying to achieve, vs. the implementation details of how the skill works.
- The description competes with other skills for Claude's attention — make it distinctive and immediately recognizable.
- If you're getting lots of failures after repeated attempts, change things up. Try different sentence structures or wordings.
I'd encourage you to be creative and mix up the style in different iterations since you'll have multiple opportunities to try different approaches and we'll just grab the highest-scoring one at the end.
Please respond with only the new description text in <new_description> tags, nothing else."""
text = _call_claude(prompt, model)
match = re.search(r"<new_description>(.*?)</new_description>", text, re.DOTALL)
description = match.group(1).strip().strip('"') if match else text.strip().strip('"')
transcript: dict = {
"iteration": iteration,
"prompt": prompt,
"response": text,
"parsed_description": description,
"char_count": len(description),
"over_limit": len(description) > 1024,
}
# Safety net: the prompt already states the 1024-char hard limit, but if
# the model blew past it anyway, make one fresh single-turn call that
# quotes the too-long version and asks for a shorter rewrite. (The old
# SDK path did this as a true multi-turn; `claude -p` is one-shot, so we
# inline the prior output into the new prompt instead.)
if len(description) > 1024:
shorten_prompt = (
f"{prompt}\n\n"
f"---\n\n"
f"A previous attempt produced this description, which at "
f"{len(description)} characters is over the 1024-character hard limit:\n\n"
f'"{description}"\n\n'
f"Rewrite it to be under 1024 characters while keeping the most "
f"important trigger words and intent coverage. Respond with only "
f"the new description in <new_description> tags."
)
shorten_text = _call_claude(shorten_prompt, model)
match = re.search(r"<new_description>(.*?)</new_description>", shorten_text, re.DOTALL)
shortened = match.group(1).strip().strip('"') if match else shorten_text.strip().strip('"')
transcript["rewrite_prompt"] = shorten_prompt
transcript["rewrite_response"] = shorten_text
transcript["rewrite_description"] = shortened
transcript["rewrite_char_count"] = len(shortened)
description = shortened
transcript["final_description"] = description
if log_dir:
log_dir.mkdir(parents=True, exist_ok=True)
log_file = log_dir / f"improve_iter_{iteration or 'unknown'}.json"
log_file.write_text(json.dumps(transcript, indent=2))
return description
def main():
parser = argparse.ArgumentParser(description="Improve a skill description based on eval results")
parser.add_argument("--eval-results", required=True, help="Path to eval results JSON (from run_eval.py)")
parser.add_argument("--skill-path", required=True, help="Path to skill directory")
parser.add_argument("--history", default=None, help="Path to history JSON (previous attempts)")
parser.add_argument("--model", required=True, help="Model for improvement")
parser.add_argument("--verbose", action="store_true", help="Print thinking to stderr")
args = parser.parse_args()
skill_path = Path(args.skill_path)
if not (skill_path / "SKILL.md").exists():
print(f"Error: No SKILL.md found at {skill_path}", file=sys.stderr)
sys.exit(1)
eval_results = json.loads(Path(args.eval_results).read_text())
history = []
if args.history:
history = json.loads(Path(args.history).read_text())
name, _, content = parse_skill_md(skill_path)
current_description = eval_results["description"]
if args.verbose:
print(f"Current: {current_description}", file=sys.stderr)
print(f"Score: {eval_results['summary']['passed']}/{eval_results['summary']['total']}", file=sys.stderr)
new_description = improve_description(
skill_name=name,
skill_content=content,
current_description=current_description,
eval_results=eval_results,
history=history,
model=args.model,
)
if args.verbose:
print(f"Improved: {new_description}", file=sys.stderr)
# Output as JSON with both the new description and updated history
output = {
"description": new_description,
"history": history + [{
"description": current_description,
"passed": eval_results["summary"]["passed"],
"failed": eval_results["summary"]["failed"],
"total": eval_results["summary"]["total"],
"results": eval_results["results"],
}],
}
print(json.dumps(output, indent=2))
if __name__ == "__main__":
main()
skills/skill-creator/scripts/package_skill.py
+136
View File
@@ -0,0 +1,136 @@
#!/usr/bin/env python3
"""
Skill Packager - Creates a distributable .skill file of a skill folder
Usage:
python utils/package_skill.py <path/to/skill-folder> [output-directory]
Example:
python utils/package_skill.py skills/public/my-skill
python utils/package_skill.py skills/public/my-skill ./dist
"""
import fnmatch
import sys
import zipfile
from pathlib import Path
from scripts.quick_validate import validate_skill
# Patterns to exclude when packaging skills.
EXCLUDE_DIRS = {"__pycache__", "node_modules"}
EXCLUDE_GLOBS = {"*.pyc"}
EXCLUDE_FILES = {".DS_Store"}
# Directories excluded only at the skill root (not when nested deeper).
ROOT_EXCLUDE_DIRS = {"evals"}
def should_exclude(rel_path: Path) -> bool:
"""Check if a path should be excluded from packaging."""
parts = rel_path.parts
if any(part in EXCLUDE_DIRS for part in parts):
return True
# rel_path is relative to skill_path.parent, so parts[0] is the skill
# folder name and parts[1] (if present) is the first subdir.
if len(parts) > 1 and parts[1] in ROOT_EXCLUDE_DIRS:
return True
name = rel_path.name
if name in EXCLUDE_FILES:
return True
return any(fnmatch.fnmatch(name, pat) for pat in EXCLUDE_GLOBS)
def package_skill(skill_path, output_dir=None):
"""
Package a skill folder into a .skill file.
Args:
skill_path: Path to the skill folder
output_dir: Optional output directory for the .skill file (defaults to current directory)
Returns:
Path to the created .skill file, or None if error
"""
skill_path = Path(skill_path).resolve()
# Validate skill folder exists
if not skill_path.exists():
print(f"❌ Error: Skill folder not found: {skill_path}")
return None
if not skill_path.is_dir():
print(f"❌ Error: Path is not a directory: {skill_path}")
return None
# Validate SKILL.md exists
skill_md = skill_path / "SKILL.md"
if not skill_md.exists():
print(f"❌ Error: SKILL.md not found in {skill_path}")
return None
# Run validation before packaging
print("🔍 Validating skill...")
valid, message = validate_skill(skill_path)
if not valid:
print(f"❌ Validation failed: {message}")
print(" Please fix the validation errors before packaging.")
return None
print(f"✅ {message}\n")
# Determine output location
skill_name = skill_path.name
if output_dir:
output_path = Path(output_dir).resolve()
output_path.mkdir(parents=True, exist_ok=True)
else:
output_path = Path.cwd()
skill_filename = output_path / f"{skill_name}.skill"
# Create the .skill file (zip format)
try:
with zipfile.ZipFile(skill_filename, 'w', zipfile.ZIP_DEFLATED) as zipf:
# Walk through the skill directory, excluding build artifacts
for file_path in skill_path.rglob('*'):
if not file_path.is_file():
continue
arcname = file_path.relative_to(skill_path.parent)
if should_exclude(arcname):
print(f" Skipped: {arcname}")
continue
zipf.write(file_path, arcname)
print(f" Added: {arcname}")
print(f"\n✅ Successfully packaged skill to: {skill_filename}")
return skill_filename
except Exception as e:
print(f"❌ Error creating .skill file: {e}")
return None
def main():
if len(sys.argv) < 2:
print("Usage: python utils/package_skill.py <path/to/skill-folder> [output-directory]")
print("\nExample:")
print(" python utils/package_skill.py skills/public/my-skill")
print(" python utils/package_skill.py skills/public/my-skill ./dist")
sys.exit(1)
skill_path = sys.argv[1]
output_dir = sys.argv[2] if len(sys.argv) > 2 else None
print(f"📦 Packaging skill: {skill_path}")
if output_dir:
print(f" Output directory: {output_dir}")
print()
result = package_skill(skill_path, output_dir)
if result:
sys.exit(0)
else:
sys.exit(1)
if __name__ == "__main__":
main()
skills/skill-creator/scripts/quick_validate.py
+103
View File
@@ -0,0 +1,103 @@
#!/usr/bin/env python3
"""
Quick validation script for skills - minimal version
"""
import sys
import os
import re
import yaml
from pathlib import Path
def validate_skill(skill_path):
"""Basic validation of a skill"""
skill_path = Path(skill_path)
# Check SKILL.md exists
skill_md = skill_path / 'SKILL.md'
if not skill_md.exists():
return False, "SKILL.md not found"
# Read and validate frontmatter
content = skill_md.read_text()
if not content.startswith('---'):
return False, "No YAML frontmatter found"
# Extract frontmatter
match = re.match(r'^---\n(.*?)\n---', content, re.DOTALL)
if not match:
return False, "Invalid frontmatter format"
frontmatter_text = match.group(1)
# Parse YAML frontmatter
try:
frontmatter = yaml.safe_load(frontmatter_text)
if not isinstance(frontmatter, dict):
return False, "Frontmatter must be a YAML dictionary"
except yaml.YAMLError as e:
return False, f"Invalid YAML in frontmatter: {e}"
# Define allowed properties
ALLOWED_PROPERTIES = {'name', 'description', 'license', 'allowed-tools', 'metadata', 'compatibility'}
# Check for unexpected properties (excluding nested keys under metadata)
unexpected_keys = set(frontmatter.keys()) - ALLOWED_PROPERTIES
if unexpected_keys:
return False, (
f"Unexpected key(s) in SKILL.md frontmatter: {', '.join(sorted(unexpected_keys))}. "
f"Allowed properties are: {', '.join(sorted(ALLOWED_PROPERTIES))}"
)
# Check required fields
if 'name' not in frontmatter:
return False, "Missing 'name' in frontmatter"
if 'description' not in frontmatter:
return False, "Missing 'description' in frontmatter"
# Extract name for validation
name = frontmatter.get('name', '')
if not isinstance(name, str):
return False, f"Name must be a string, got {type(name).__name__}"
name = name.strip()
if name:
# Check naming convention (kebab-case: lowercase with hyphens)
if not re.match(r'^[a-z0-9-]+$', name):
return False, f"Name '{name}' should be kebab-case (lowercase letters, digits, and hyphens only)"
if name.startswith('-') or name.endswith('-') or '--' in name:
return False, f"Name '{name}' cannot start/end with hyphen or contain consecutive hyphens"
# Check name length (max 64 characters per spec)
if len(name) > 64:
return False, f"Name is too long ({len(name)} characters). Maximum is 64 characters."
# Extract and validate description
description = frontmatter.get('description', '')
if not isinstance(description, str):
return False, f"Description must be a string, got {type(description).__name__}"
description = description.strip()
if description:
# Check for angle brackets
if '<' in description or '>' in description:
return False, "Description cannot contain angle brackets (< or >)"
# Check description length (max 1024 characters per spec)
if len(description) > 1024:
return False, f"Description is too long ({len(description)} characters). Maximum is 1024 characters."
# Validate compatibility field if present (optional)
compatibility = frontmatter.get('compatibility', '')
if compatibility:
if not isinstance(compatibility, str):
return False, f"Compatibility must be a string, got {type(compatibility).__name__}"
if len(compatibility) > 500:
return False, f"Compatibility is too long ({len(compatibility)} characters). Maximum is 500 characters."
return True, "Skill is valid!"
if __name__ == "__main__":
if len(sys.argv) != 2:
print("Usage: python quick_validate.py <skill_directory>")
sys.exit(1)
valid, message = validate_skill(sys.argv[1])
print(message)
sys.exit(0 if valid else 1)
skills/skill-creator/scripts/run_eval.py
+310
View File
@@ -0,0 +1,310 @@
#!/usr/bin/env python3
"""Run trigger evaluation for a skill description.
Tests whether a skill's description causes Claude to trigger (read the skill)
for a set of queries. Outputs results as JSON.
"""
import argparse
import json
import os
import select
import subprocess
import sys
import time
import uuid
from concurrent.futures import ProcessPoolExecutor, as_completed
from pathlib import Path
from scripts.utils import parse_skill_md
def find_project_root() -> Path:
"""Find the project root by walking up from cwd looking for .claude/.
Mimics how Claude Code discovers its project root, so the command file
we create ends up where claude -p will look for it.
"""
current = Path.cwd()
for parent in [current, *current.parents]:
if (parent / ".claude").is_dir():
return parent
return current
def run_single_query(
query: str,
skill_name: str,
skill_description: str,
timeout: int,
project_root: str,
model: str | None = None,
) -> bool:
"""Run a single query and return whether the skill was triggered.
Creates a command file in .claude/commands/ so it appears in Claude's
available_skills list, then runs `claude -p` with the raw query.
Uses --include-partial-messages to detect triggering early from
stream events (content_block_start) rather than waiting for the
full assistant message, which only arrives after tool execution.
"""
unique_id = uuid.uuid4().hex[:8]
clean_name = f"{skill_name}-skill-{unique_id}"
project_commands_dir = Path(project_root) / ".claude" / "commands"
command_file = project_commands_dir / f"{clean_name}.md"
try:
project_commands_dir.mkdir(parents=True, exist_ok=True)
# Use YAML block scalar to avoid breaking on quotes in description
indented_desc = "\n ".join(skill_description.split("\n"))
command_content = (
f"---\n"
f"description: |\n"
f" {indented_desc}\n"
f"---\n\n"
f"# {skill_name}\n\n"
f"This skill handles: {skill_description}\n"
)
command_file.write_text(command_content)
cmd = [
"claude",
"-p", query,
"--output-format", "stream-json",
"--verbose",
"--include-partial-messages",
]
if model:
cmd.extend(["--model", model])
# Remove CLAUDECODE env var to allow nesting claude -p inside a
# Claude Code session. The guard is for interactive terminal conflicts;
# programmatic subprocess usage is safe.
env = {k: v for k, v in os.environ.items() if k != "CLAUDECODE"}
process = subprocess.Popen(
cmd,
stdout=subprocess.PIPE,
stderr=subprocess.DEVNULL,
cwd=project_root,
env=env,
)
triggered = False
start_time = time.time()
buffer = ""
# Track state for stream event detection
pending_tool_name = None
accumulated_json = ""
try:
while time.time() - start_time < timeout:
if process.poll() is not None:
remaining = process.stdout.read()
if remaining:
buffer += remaining.decode("utf-8", errors="replace")
break
ready, _, _ = select.select([process.stdout], [], [], 1.0)
if not ready:
continue
chunk = os.read(process.stdout.fileno(), 8192)
if not chunk:
break
buffer += chunk.decode("utf-8", errors="replace")
while "\n" in buffer:
line, buffer = buffer.split("\n", 1)
line = line.strip()
if not line:
continue
try:
event = json.loads(line)
except json.JSONDecodeError:
continue
# Early detection via stream events
if event.get("type") == "stream_event":
se = event.get("event", {})
se_type = se.get("type", "")
if se_type == "content_block_start":
cb = se.get("content_block", {})
if cb.get("type") == "tool_use":
tool_name = cb.get("name", "")
if tool_name in ("Skill", "Read"):
pending_tool_name = tool_name
accumulated_json = ""
else:
return False
elif se_type == "content_block_delta" and pending_tool_name:
delta = se.get("delta", {})
if delta.get("type") == "input_json_delta":
accumulated_json += delta.get("partial_json", "")
if clean_name in accumulated_json:
return True
elif se_type in ("content_block_stop", "message_stop"):
if pending_tool_name:
return clean_name in accumulated_json
if se_type == "message_stop":
return False
# Fallback: full assistant message
elif event.get("type") == "assistant":
message = event.get("message", {})
for content_item in message.get("content", []):
if content_item.get("type") != "tool_use":
continue
tool_name = content_item.get("name", "")
tool_input = content_item.get("input", {})
if tool_name == "Skill" and clean_name in tool_input.get("skill", ""):
triggered = True
elif tool_name == "Read" and clean_name in tool_input.get("file_path", ""):
triggered = True
return triggered
elif event.get("type") == "result":
return triggered
finally:
# Clean up process on any exit path (return, exception, timeout)
if process.poll() is None:
process.kill()
process.wait()
return triggered
finally:
if command_file.exists():
command_file.unlink()
def run_eval(
eval_set: list[dict],
skill_name: str,
description: str,
num_workers: int,
timeout: int,
project_root: Path,
runs_per_query: int = 1,
trigger_threshold: float = 0.5,
model: str | None = None,
) -> dict:
"""Run the full eval set and return results."""
results = []
with ProcessPoolExecutor(max_workers=num_workers) as executor:
future_to_info = {}
for item in eval_set:
for run_idx in range(runs_per_query):
future = executor.submit(
run_single_query,
item["query"],
skill_name,
description,
timeout,
str(project_root),
model,
)
future_to_info[future] = (item, run_idx)
query_triggers: dict[str, list[bool]] = {}
query_items: dict[str, dict] = {}
for future in as_completed(future_to_info):
item, _ = future_to_info[future]
query = item["query"]
query_items[query] = item
if query not in query_triggers:
query_triggers[query] = []
try:
query_triggers[query].append(future.result())
except Exception as e:
print(f"Warning: query failed: {e}", file=sys.stderr)
query_triggers[query].append(False)
for query, triggers in query_triggers.items():
item = query_items[query]
trigger_rate = sum(triggers) / len(triggers)
should_trigger = item["should_trigger"]
if should_trigger:
did_pass = trigger_rate >= trigger_threshold
else:
did_pass = trigger_rate < trigger_threshold
results.append({
"query": query,
"should_trigger": should_trigger,
"trigger_rate": trigger_rate,
"triggers": sum(triggers),
"runs": len(triggers),
"pass": did_pass,
})
passed = sum(1 for r in results if r["pass"])
total = len(results)
return {
"skill_name": skill_name,
"description": description,
"results": results,
"summary": {
"total": total,
"passed": passed,
"failed": total - passed,
},
}
def main():
parser = argparse.ArgumentParser(description="Run trigger evaluation for a skill description")
parser.add_argument("--eval-set", required=True, help="Path to eval set JSON file")
parser.add_argument("--skill-path", required=True, help="Path to skill directory")
parser.add_argument("--description", default=None, help="Override description to test")
parser.add_argument("--num-workers", type=int, default=10, help="Number of parallel workers")
parser.add_argument("--timeout", type=int, default=30, help="Timeout per query in seconds")
parser.add_argument("--runs-per-query", type=int, default=3, help="Number of runs per query")
parser.add_argument("--trigger-threshold", type=float, default=0.5, help="Trigger rate threshold")
parser.add_argument("--model", default=None, help="Model to use for claude -p (default: user's configured model)")
parser.add_argument("--verbose", action="store_true", help="Print progress to stderr")
args = parser.parse_args()
eval_set = json.loads(Path(args.eval_set).read_text())
skill_path = Path(args.skill_path)
if not (skill_path / "SKILL.md").exists():
print(f"Error: No SKILL.md found at {skill_path}", file=sys.stderr)
sys.exit(1)
name, original_description, content = parse_skill_md(skill_path)
description = args.description or original_description
project_root = find_project_root()
if args.verbose:
print(f"Evaluating: {description}", file=sys.stderr)
output = run_eval(
eval_set=eval_set,
skill_name=name,
description=description,
num_workers=args.num_workers,
timeout=args.timeout,
project_root=project_root,
runs_per_query=args.runs_per_query,
trigger_threshold=args.trigger_threshold,
model=args.model,
)
if args.verbose:
summary = output["summary"]
print(f"Results: {summary['passed']}/{summary['total']} passed", file=sys.stderr)
for r in output["results"]:
status = "PASS" if r["pass"] else "FAIL"
rate_str = f"{r['triggers']}/{r['runs']}"
print(f" [{status}] rate={rate_str} expected={r['should_trigger']}: {r['query'][:70]}", file=sys.stderr)
print(json.dumps(output, indent=2))
if __name__ == "__main__":
main()
skills/skill-creator/scripts/run_loop.py
+328
View File
@@ -0,0 +1,328 @@
#!/usr/bin/env python3
"""Run the eval + improve loop until all pass or max iterations reached.
Combines run_eval.py and improve_description.py in a loop, tracking history
and returning the best description found. Supports train/test split to prevent
overfitting.
"""
import argparse
import json
import random
import sys
import tempfile
import time
import webbrowser
from pathlib import Path
from scripts.generate_report import generate_html
from scripts.improve_description import improve_description
from scripts.run_eval import find_project_root, run_eval
from scripts.utils import parse_skill_md
def split_eval_set(eval_set: list[dict], holdout: float, seed: int = 42) -> tuple[list[dict], list[dict]]:
"""Split eval set into train and test sets, stratified by should_trigger."""
random.seed(seed)
# Separate by should_trigger
trigger = [e for e in eval_set if e["should_trigger"]]
no_trigger = [e for e in eval_set if not e["should_trigger"]]
# Shuffle each group
random.shuffle(trigger)
random.shuffle(no_trigger)
# Calculate split points
n_trigger_test = max(1, int(len(trigger) * holdout))
n_no_trigger_test = max(1, int(len(no_trigger) * holdout))
# Split
test_set = trigger[:n_trigger_test] + no_trigger[:n_no_trigger_test]
train_set = trigger[n_trigger_test:] + no_trigger[n_no_trigger_test:]
return train_set, test_set
def run_loop(
eval_set: list[dict],
skill_path: Path,
description_override: str | None,
num_workers: int,
timeout: int,
max_iterations: int,
runs_per_query: int,
trigger_threshold: float,
holdout: float,
model: str,
verbose: bool,
live_report_path: Path | None = None,
log_dir: Path | None = None,
) -> dict:
"""Run the eval + improvement loop."""
project_root = find_project_root()
name, original_description, content = parse_skill_md(skill_path)
current_description = description_override or original_description
# Split into train/test if holdout > 0
if holdout > 0:
train_set, test_set = split_eval_set(eval_set, holdout)
if verbose:
print(f"Split: {len(train_set)} train, {len(test_set)} test (holdout={holdout})", file=sys.stderr)
else:
train_set = eval_set
test_set = []
history = []
exit_reason = "unknown"
for iteration in range(1, max_iterations + 1):
if verbose:
print(f"\n{'='*60}", file=sys.stderr)
print(f"Iteration {iteration}/{max_iterations}", file=sys.stderr)
print(f"Description: {current_description}", file=sys.stderr)
print(f"{'='*60}", file=sys.stderr)
# Evaluate train + test together in one batch for parallelism
all_queries = train_set + test_set
t0 = time.time()
all_results = run_eval(
eval_set=all_queries,
skill_name=name,
description=current_description,
num_workers=num_workers,
timeout=timeout,
project_root=project_root,
runs_per_query=runs_per_query,
trigger_threshold=trigger_threshold,
model=model,
)
eval_elapsed = time.time() - t0
# Split results back into train/test by matching queries
train_queries_set = {q["query"] for q in train_set}
train_result_list = [r for r in all_results["results"] if r["query"] in train_queries_set]
test_result_list = [r for r in all_results["results"] if r["query"] not in train_queries_set]
train_passed = sum(1 for r in train_result_list if r["pass"])
train_total = len(train_result_list)
train_summary = {"passed": train_passed, "failed": train_total - train_passed, "total": train_total}
train_results = {"results": train_result_list, "summary": train_summary}
if test_set:
test_passed = sum(1 for r in test_result_list if r["pass"])
test_total = len(test_result_list)
test_summary = {"passed": test_passed, "failed": test_total - test_passed, "total": test_total}
test_results = {"results": test_result_list, "summary": test_summary}
else:
test_results = None
test_summary = None
history.append({
"iteration": iteration,
"description": current_description,
"train_passed": train_summary["passed"],
"train_failed": train_summary["failed"],
"train_total": train_summary["total"],
"train_results": train_results["results"],
"test_passed": test_summary["passed"] if test_summary else None,
"test_failed": test_summary["failed"] if test_summary else None,
"test_total": test_summary["total"] if test_summary else None,
"test_results": test_results["results"] if test_results else None,
# For backward compat with report generator
"passed": train_summary["passed"],
"failed": train_summary["failed"],
"total": train_summary["total"],
"results": train_results["results"],
})
# Write live report if path provided
if live_report_path:
partial_output = {
"original_description": original_description,
"best_description": current_description,
"best_score": "in progress",
"iterations_run": len(history),
"holdout": holdout,
"train_size": len(train_set),
"test_size": len(test_set),
"history": history,
}
live_report_path.write_text(generate_html(partial_output, auto_refresh=True, skill_name=name))
if verbose:
def print_eval_stats(label, results, elapsed):
pos = [r for r in results if r["should_trigger"]]
neg = [r for r in results if not r["should_trigger"]]
tp = sum(r["triggers"] for r in pos)
pos_runs = sum(r["runs"] for r in pos)
fn = pos_runs - tp
fp = sum(r["triggers"] for r in neg)
neg_runs = sum(r["runs"] for r in neg)
tn = neg_runs - fp
total = tp + tn + fp + fn
precision = tp / (tp + fp) if (tp + fp) > 0 else 1.0
recall = tp / (tp + fn) if (tp + fn) > 0 else 1.0
accuracy = (tp + tn) / total if total > 0 else 0.0
print(f"{label}: {tp+tn}/{total} correct, precision={precision:.0%} recall={recall:.0%} accuracy={accuracy:.0%} ({elapsed:.1f}s)", file=sys.stderr)
for r in results:
status = "PASS" if r["pass"] else "FAIL"
rate_str = f"{r['triggers']}/{r['runs']}"
print(f" [{status}] rate={rate_str} expected={r['should_trigger']}: {r['query'][:60]}", file=sys.stderr)
print_eval_stats("Train", train_results["results"], eval_elapsed)
if test_summary:
print_eval_stats("Test ", test_results["results"], 0)
if train_summary["failed"] == 0:
exit_reason = f"all_passed (iteration {iteration})"
if verbose:
print(f"\nAll train queries passed on iteration {iteration}!", file=sys.stderr)
break
if iteration == max_iterations:
exit_reason = f"max_iterations ({max_iterations})"
if verbose:
print(f"\nMax iterations reached ({max_iterations}).", file=sys.stderr)
break
# Improve the description based on train results
if verbose:
print(f"\nImproving description...", file=sys.stderr)
t0 = time.time()
# Strip test scores from history so improvement model can't see them
blinded_history = [
{k: v for k, v in h.items() if not k.startswith("test_")}
for h in history
]
new_description = improve_description(
skill_name=name,
skill_content=content,
current_description=current_description,
eval_results=train_results,
history=blinded_history,
model=model,
log_dir=log_dir,
iteration=iteration,
)
improve_elapsed = time.time() - t0
if verbose:
print(f"Proposed ({improve_elapsed:.1f}s): {new_description}", file=sys.stderr)
current_description = new_description
# Find the best iteration by TEST score (or train if no test set)
if test_set:
best = max(history, key=lambda h: h["test_passed"] or 0)
best_score = f"{best['test_passed']}/{best['test_total']}"
else:
best = max(history, key=lambda h: h["train_passed"])
best_score = f"{best['train_passed']}/{best['train_total']}"
if verbose:
print(f"\nExit reason: {exit_reason}", file=sys.stderr)
print(f"Best score: {best_score} (iteration {best['iteration']})", file=sys.stderr)
return {
"exit_reason": exit_reason,
"original_description": original_description,
"best_description": best["description"],
"best_score": best_score,
"best_train_score": f"{best['train_passed']}/{best['train_total']}",
"best_test_score": f"{best['test_passed']}/{best['test_total']}" if test_set else None,
"final_description": current_description,
"iterations_run": len(history),
"holdout": holdout,
"train_size": len(train_set),
"test_size": len(test_set),
"history": history,
}
def main():
parser = argparse.ArgumentParser(description="Run eval + improve loop")
parser.add_argument("--eval-set", required=True, help="Path to eval set JSON file")
parser.add_argument("--skill-path", required=True, help="Path to skill directory")
parser.add_argument("--description", default=None, help="Override starting description")
parser.add_argument("--num-workers", type=int, default=10, help="Number of parallel workers")
parser.add_argument("--timeout", type=int, default=30, help="Timeout per query in seconds")
parser.add_argument("--max-iterations", type=int, default=5, help="Max improvement iterations")
parser.add_argument("--runs-per-query", type=int, default=3, help="Number of runs per query")
parser.add_argument("--trigger-threshold", type=float, default=0.5, help="Trigger rate threshold")
parser.add_argument("--holdout", type=float, default=0.4, help="Fraction of eval set to hold out for testing (0 to disable)")
parser.add_argument("--model", required=True, help="Model for improvement")
parser.add_argument("--verbose", action="store_true", help="Print progress to stderr")
parser.add_argument("--report", default="auto", help="Generate HTML report at this path (default: 'auto' for temp file, 'none' to disable)")
parser.add_argument("--results-dir", default=None, help="Save all outputs (results.json, report.html, log.txt) to a timestamped subdirectory here")
args = parser.parse_args()
eval_set = json.loads(Path(args.eval_set).read_text())
skill_path = Path(args.skill_path)
if not (skill_path / "SKILL.md").exists():
print(f"Error: No SKILL.md found at {skill_path}", file=sys.stderr)
sys.exit(1)
name, _, _ = parse_skill_md(skill_path)
# Set up live report path
if args.report != "none":
if args.report == "auto":
timestamp = time.strftime("%Y%m%d_%H%M%S")
live_report_path = Path(tempfile.gettempdir()) / f"skill_description_report_{skill_path.name}_{timestamp}.html"
else:
live_report_path = Path(args.report)
# Open the report immediately so the user can watch
live_report_path.write_text("<html><body><h1>Starting optimization loop...</h1><meta http-equiv='refresh' content='5'></body></html>")
webbrowser.open(str(live_report_path))
else:
live_report_path = None
# Determine output directory (create before run_loop so logs can be written)
if args.results_dir:
timestamp = time.strftime("%Y-%m-%d_%H%M%S")
results_dir = Path(args.results_dir) / timestamp
results_dir.mkdir(parents=True, exist_ok=True)
else:
results_dir = None
log_dir = results_dir / "logs" if results_dir else None
output = run_loop(
eval_set=eval_set,
skill_path=skill_path,
description_override=args.description,
num_workers=args.num_workers,
timeout=args.timeout,
max_iterations=args.max_iterations,
runs_per_query=args.runs_per_query,
trigger_threshold=args.trigger_threshold,
holdout=args.holdout,
model=args.model,
verbose=args.verbose,
live_report_path=live_report_path,
log_dir=log_dir,
)
# Save JSON output
json_output = json.dumps(output, indent=2)
print(json_output)
if results_dir:
(results_dir / "results.json").write_text(json_output)
# Write final HTML report (without auto-refresh)
if live_report_path:
live_report_path.write_text(generate_html(output, auto_refresh=False, skill_name=name))
print(f"\nReport: {live_report_path}", file=sys.stderr)
if results_dir and live_report_path:
(results_dir / "report.html").write_text(generate_html(output, auto_refresh=False, skill_name=name))
if results_dir:
print(f"Results saved to: {results_dir}", file=sys.stderr)
if __name__ == "__main__":
main()
skills/skill-creator/scripts/utils.py
+47
View File
@@ -0,0 +1,47 @@
"""Shared utilities for skill-creator scripts."""
from pathlib import Path
def parse_skill_md(skill_path: Path) -> tuple[str, str, str]:
"""Parse a SKILL.md file, returning (name, description, full_content)."""
content = (skill_path / "SKILL.md").read_text()
lines = content.split("\n")
if lines[0].strip() != "---":
raise ValueError("SKILL.md missing frontmatter (no opening ---)")
end_idx = None
for i, line in enumerate(lines[1:], start=1):
if line.strip() == "---":
end_idx = i
break
if end_idx is None:
raise ValueError("SKILL.md missing frontmatter (no closing ---)")
name = ""
description = ""
frontmatter_lines = lines[1:end_idx]
i = 0
while i < len(frontmatter_lines):
line = frontmatter_lines[i]
if line.startswith("name:"):
name = line[len("name:"):].strip().strip('"').strip("'")
elif line.startswith("description:"):
value = line[len("description:"):].strip()
# Handle YAML multiline indicators (>, |, >-, |-)
if value in (">", "|", ">-", "|-"):
continuation_lines: list[str] = []
i += 1
while i < len(frontmatter_lines) and (frontmatter_lines[i].startswith(" ") or frontmatter_lines[i].startswith("\t")):
continuation_lines.append(frontmatter_lines[i].strip())
i += 1
description = " ".join(continuation_lines)
continue
else:
description = value.strip('"').strip("'")
i += 1
return name, description, content