github-intel

What This Skill Does

The github-intel skill acts as a powerful reconnaissance engine for software repositories. It provides AI agents with the ability to ingest, structure, and visualize GitHub project data without ever needing to download, compile, or execute untrusted code. By bridging the gap between raw repository files and machine-readable data formats, this skill allows for instant code architecture reviews, documentation generation, and rapid codebase onboarding. Whether you are performing security research, evaluating a new open-source library, or auditing a legacy project, this tool extracts the essence of any repository into a clean, searchable format.

Installation

To install this skill, run the following command in your terminal: clawhub install openclaw/skills/skills/aiwithabidi/a6-github-intel

Ensure that you have Python 3 installed in your local workspace. While the skill runs perfectly without credentials, providing a GITHUB_TOKEN is highly recommended for developers or agents performing high-frequency analysis to avoid rate-limiting by the GitHub API.

Use Cases

• Codebase Audits: Quickly identify the file structure and primary languages of a project before diving into deep analysis.
• Documentation Generation: Automatically generate professional-grade Markdown documentation from complex repositories.
• Architecture Visualization: Use Mermaid diagrams to understand how modules and components interact without manually drawing flowcharts.
• Open Source Research: Track the pulse of a repository by monitoring recent commit history, contributor activity, and release patterns.
• Quick Onboarding: Get an immediate summary of an unknown repo by converting it to a single, concise AI-friendly document.

Example Prompts

1. "Analyze the repository https://github.com/anthropics/claude-code and generate a Mermaid flowchart describing its main command-line interaction flow."
2. "Convert the entire repository at https://github.com/openai/openai-python into a single markdown document so I can query its API patterns."
3. "Give me a report on the activity and language breakdown for https://github.com/microsoft/autogen to help me decide if it's the right fit for my project."

Tips & Limitations

• Rate Limiting: If you do not provide a GITHUB_TOKEN, you are limited to 60 requests per hour. For deep crawls or multiple repository analyses, always add your token.
• Depth Control: Use the --depth flag to avoid overwhelming the model with thousands of lines of boilerplate code in large projects.
• Read-Only: This skill is strictly for analysis. It cannot clone full git histories or execute code; it is explicitly designed for safe, read-only intelligence gathering.
• Storage: For very large repositories, prefer generating summaries or specific file trees rather than the full markdown conversion to save memory.