GitHub to Skills Factory

This skill automates the conversion of GitHub repositories into fully functional AI skills.

Core Functionality Analysis: Fetches repository metadata (Description, README, Latest Commit Hash). Scaffolding: Creates a standardized skill directory structure. Metadata Injection: Generates SKILL.md with extended frontmatter (tracking source, version, hash) for future automated management. Wrapper Generation: Creates a scripts/wrapper.py (or similar) to interface with the tool. Usage

Trigger: /GitHub-to-skills or "Package this repo into a skill: "

Required Metadata Schema

Every skill created by this factory MUST include the following extended YAML frontmatter in its SKILL.md. This is critical for the skill-manager to function later.

name: description:

EXTENDED METADATA (MANDATORY)

github_url: github_hash: version: created_at: entry_point: scripts/wrapper.py # or main script dependencies: # List main dependencies if known, e.g., ["yt-dlp", "ffmpeg"]

Workflow Fetch Info: The agent first runs scripts/fetch_github_info.py to get the raw data from the repo. Plan: The agent analyzes the README to understand how to invoke the tool (CLI args, Python API, etc.). Generate: The agent uses the skill-creator patterns to write the SKILL.md and wrapper scripts, ensuring the extended metadata is present. Verify: Checks if the commit hash was correctly captured. Resources scripts/fetch_github_info.py: Utility to scrape/API fetch repo details (README, Hash, Tags). scripts/create_github_skill.py: Orchestrator to scaffold the folder and write the initial files. Best Practices for Generated Skills Isolation: The generated skill should install its own dependencies (e.g., in a venv or via uv/pip) if possible, or clearly state them. Progressive Disclosure: Do not dump the entire repo into the skill. Only include the necessary wrapper code and reference the original repo for deep dives. Idempotency: The github_hash field allows the future skill-manager to check if remote_hash != local_hash to trigger updates.