A professional AI research tool that generates comprehensive technical reports for any GitHub repository.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install github-research-assistant
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install github-research-assistant using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is GitHub Research Assistant?
The GitHub Research Assistant is a powerful utility within the Openclaw Skills ecosystem designed to accelerate the way developers and researchers understand open-source codebases. Instead of manually auditing folders and documentation, this skill automates the extraction of project health metrics, core functionality, and underlying technology choices. It provides a systematic approach to repository discovery, ensuring you get a high-level overview and a deep technical deep dive in seconds.
By utilizing Openclaw Skills, this assistant bridges the gap between raw source code and actionable intelligence. It evaluates everything from basic metadata like stars and forks to complex architectural patterns and module dependencies, making it an essential tool for developers evaluating new libraries or onboarding to unfamiliar projects.
GitHub Research Assistant Use Cases
- Rapidly evaluating the technical viability of a third-party library for a new project.
- Conducting competitive analysis on open-source tools within a specific niche.
- Speeding up developer onboarding by generating architectural overviews of complex repositories.
- Summarizing repository purpose and tech stacks for technical documentation or reports using Openclaw Skills.
How GitHub Research Assistant Works
- The skill initiates by fetching basic repository metadata including star counts, fork counts, and the most recent commit activity.
- It identifies and reads critical project files such as README.md, package.json, pyproject.toml, or Cargo.toml to determine the project scope and dependencies.
- The assistant scans the directory structure to infer the technical architecture and primary module responsibilities.
- It synthesizes all gathered data into a structured report covering basic info, purpose, tech stack, usage, and architecture.
- The final output is delivered as a clean, professional Markdown report via Openclaw Skills.
GitHub Research Assistant Setup
To enable the GitHub Research Assistant, you need to add it to your agent configuration. Ensure your environment has access to the necessary repository scanning tools provided by Openclaw Skills.
# Example of adding the skill to your agent environment
openclaw install github-research-assistant
No complex configuration is required as the skill automatically leverages standard project manifests to identify tech stacks.
GitHub Research Assistant Data Schema & Taxonomy
The GitHub Research Assistant organizes information into a five-dimensional schema to ensure consistency across reports generated by Openclaw Skills:
| Dimension | Data Points Included |
|---|---|
| Basic Info | URL, Stars, Forks, Last Commit, One-sentence description |
| Repository Purpose | Use cases, pain points solved, core APIs, I/O formats |
| Tech Stack | Languages, frameworks, key libraries, algorithms, build tools |
| Usage | Installation steps, environment requirements, executable examples |
| Architecture | Directory structure, module responsibilities, data flow design |
GitHub Research Assistant Advanced Features
- Intelligent manifest parsing to automatically detect language versions and framework dependencies.
- Architectural inference that maps directory structures to logical module responsibilities.
- Multi-ecosystem support covering Python, JavaScript, Rust, and more through Openclaw Skills integration.
- Optimized for large-scale repositories by prioritizing core logic files and entry points over boilerplate code.
SKILL.md
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