DataShrimp/skills

Source Provenance

• Official Repository: anthropics/skills — mcp-builder/SKILL.md
• Additional Skill Engineering References Included:
  • Claude Code Skills — Dynamic Context Injection (!command``)
  • OpenAI ChatGPT & Codex Skills (agents/openai.yaml)
  • Agent Skills Specification — Script Harness (agentskills.io)

---

Part 1: Anthropic Official mcp-builder/SKILL.md (3-Level Progressive Disclosure Exemplar)

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.

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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 - Core guidelines for all MCP servers
• For TypeScript (recommended): ⚡ TypeScript Guide - TypeScript patterns and examples
• For Python: 🐍 Python Guide - Python/FastMCP 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 web fetch as needed.

Tool Selection: Prioritize comprehensive API coverage. List endpoints to implement, starting with the most common operations.

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Phase 2: Implementation

2.1 Set Up Project Structure

See language-specific guides for project setup:

• ⚡ TypeScript Guide - Project structure, package.json, tsconfig.json
• 🐍 Python Guide - Module organization, dependencies

2.2 Implement Core Infrastructure

Create shared utilities:

• API client with authentication
• Error handling helpers
• Response formatting (JSON and 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 value 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.

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Phase 4: Create Evaluations

After implementing your MCP server, create comprehensive evaluations to test its effectiveness.

Load ✅ Evaluation Guide 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:

<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 - 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
• ⚡ TypeScript Implementation Guide

Evaluation Guide (Load During Phase 4)

• ✅ Evaluation Guide

---

Part 2: Claude Code Dynamic Context Injection (!command``) Skill Exemplar

• Source: Claude Code Skills Documentation

---
name: pr-summary
description: Summarize changes in a pull request. Use when the user asks to review, summarize, or audit the current PR or branch diff.
context: fork
agent: Explore
allowed-tools: Bash(gh *)
---

## Pull request context
- PR diff: !`gh pr diff`
- PR comments: !`gh pr view --comments`
- Changed files: !`gh pr diff --name-only`

## Your task
Summarize this pull request with concrete file-level risks, breaking changes, and verification steps...

---

Part 3: OpenAI Codex / ChatGPT agents/openai.yaml Sidecar Schema

• Source: OpenAI Build Skills Documentation

interface:
  display_name: "Self-Evolving Meta-Prompt & Role Architect"
  short_description: "Compile, evaluate, and adapt Prompts, Skills, and Agent Roles across tools"
  icon_small: "./assets/icon-small.svg"
  icon_large: "./assets/icon-large.png"
  brand_color: "#2563EB"
  default_prompt: "Design or optimize a prompt, skill, or agent role definition with an eval scorecard."

policy:
  allow_implicit_invocation: true

dependencies:
  tools:
    - type: "mcp"
      value: "git"
      description: "Git MCP server for repository inspection"

---

Part 4: Agent Skills PEP 723 Self-Contained Script Harness Exemplar

• Source: Agent Skills — Using Scripts (agentskills.io)

# /// script
# dependencies = [
#   "pydantic>=2.0",
# ]
# requires-python = ">=3.10"
# ///
"""Self-contained non-interactive CLI script designed for Agent Skill invocation via `uv run`."""

import argparse
import json
import sys


def main() -> int:
    parser = argparse.ArgumentParser(
        description="Validate skill or role frontmatter and token budget without interactive prompts."
    )
    parser.add_argument("--input", required=True, help="Path to target Markdown file")
    parser.add_argument("--max-chars", type=int, default=12000, help="Character budget ceiling")
    parser.add_argument("--dry-run", action="store_true", help="Preview checks without writing")
    args = parser.parse_args()

    try:
        with open(args.input, "r", encoding="utf-8") as f:
            text = f.read()
    except FileNotFoundError:
        sys.stderr.write(
            f"Error: File '{args.input}' not found. Pass a valid path via --input <path>.\n"
        )
        return 1

    char_count = len(text)
    passed = char_count <= args.max_chars
    result = {
        "file": args.input,
        "char_count": char_count,
        "max_chars": args.max_chars,
        "passed": passed,
        "remediation": None if passed else f"Prune {char_count - args.max_chars} chars or move details to references/.",
    }
    sys.stdout.write(json.dumps(result, ensure_ascii=False, indent=2) + "\n")
    return 0 if passed else 2


if __name__ == "__main__":
    sys.exit(main())