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📦 This repository is included in openclaw-skills — an aggregator repo with more skills. Star it to get all updates.


GitHub Explorer

An OpenClaw Agent Skill that performs deep, multi-source analysis of any GitHub project and outputs a structured research report.

Overview

GitHub Explorer goes beyond a project's README. It automatically collects information from GitHub Issues, commits, Chinese developer communities (Zhihu, WeChat, V2EX), tech blogs (Medium, Dev.to), and Twitter/X discussions — then applies AI-driven judgment to produce a high-density, decision-ready report.

Features

  • Multi-source collection — Pulls from GitHub Issues, commits, Chinese communities (Zhihu / V2EX / WeChat), tech blogs (Medium / Dev.to), and Twitter discussions in parallel
  • AI-driven judgment — Automatically classifies project stage (early experimental / rapid growth / mature stable / maintenance mode / stalled) and surfaces high-quality Issues
  • Intent-aware search — Powered by search-layer v2; selects search strategy by context (project research, competitive comparison, latest updates, etc.) with results ranked by authority, freshness, and keyword relevance
  • Competitive analysis — Identifies projects in the same space and produces a side-by-side comparison with links
  • Knowledge graph check — Verifies coverage in DeepWiki, Zread.ai, and other knowledge sources
  • Community sentiment — Quotes specific tweets and forum posts with source links; no vague summaries
  • Structured output — Fixed report template for fast, consistent decision-making

Installation

Recommended (npx)

npx skills add https://github.com/Arxchibobo/openclaw-github-explorer

Manual

cd ~/.openclaw/skills/
git clone https://github.com/Arxchibobo/openclaw-github-explorer github-explorer

Dependency Skills

GitHub Explorer uses the following OpenClaw built-in tools (no extra install needed): web_search, web_fetch, browser

The following optional skills unlock stronger search and extraction capabilities. They are bundled in openclaw-search-skills:

Skill Purpose
search-layer Multi-source search with intent-aware scoring (Brave + Exa + Tavily); v2 supports --intent, --queries, --freshness, --domain-boost
content-extract High-fidelity content extraction; fallback for anti-scraping sites (WeChat, Zhihu, etc.)
mineru-extract MinerU official API wrapper (downstream of content-extract)

Install all dependencies at once:

# Tell your OpenClaw agent:
Install this skill: https://github.com/blessonism/openclaw-search-skills

Or manually:

git clone https://github.com/blessonism/openclaw-search-skills.git /tmp/openclaw-search-skills
cd ~/.openclaw/workspace/skills
ln -s /tmp/openclaw-search-skills/search-layer search-layer
ln -s /tmp/openclaw-search-skills/content-extract content-extract
ln -s /tmp/openclaw-search-skills/mineru-extract mineru-extract

If dependency skills are missing, github-explorer automatically falls back to built-in tools. Core functionality is not affected.

Usage

After installation, trigger the skill by asking your OpenClaw Agent in natural language:

Analyze the langchain project for me
Take a look at https://github.com/microsoft/graphrag
What's the deal with the ollama project?

The agent auto-triggers GitHub Explorer, runs multi-source collection and analysis, and outputs a complete research report.

Report Structure

The report title links to the GitHub repository. Sections:

Section Content
One-liner positioning What the project is and what problem it solves
Core mechanism Technical approach / architecture in plain language
Project health Stars, Forks, License, team, commit trends, recent activity
Selected Issues Top 3–5 high-quality Issues with links and key discussion points
Use cases When you should use it
Limitations When you should not use it
Competitive comparison Similar projects with differences and links
Knowledge graph DeepWiki / Zread.ai coverage status
Demo Live demo links
Related papers arXiv links
Community sentiment Specific Twitter and Chinese community quotes with source links
Final judgment Subjective assessment and recommendation

Design Principles

  1. Full traceability — Every claim includes a source link so readers can verify
  2. Intent-aware search — Different collection phases use different search intents (exploratory for research, comparison for competitive analysis, status for latest updates), with results weighted by authority, freshness, and relevance
  3. Graceful degradation — When web_fetch fails, automatically escalates to content-extract (MinerU) to maintain content quality
  4. No vague claims — Community sentiment must cite specific content; summaries like "well received" are rejected
  5. Parallel collection — Multiple sources and queries run concurrently (--queries) for efficiency

Requirements

License

MIT


Made with by blessonism & Ms.Q

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