github-deep-research

Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source projects.

72,875stars9,863forksUpdated 6/22/2026

Data Science & ML Git & Version Control Documentation #research #analysis #documentation #github #markdown #open-source

Security Assessment

Safe(100/100)

Security Score100/100

About github-deep-research

The GitHub Deep Research skill conducts comprehensive, multi-round investigations of GitHub repositories and open source projects. It combines GitHub API data extraction, web searches, and content fetching to produce detailed markdown reports with executive summaries, chronological timelines, metrics analysis, and visual diagrams. This skill solves the problem of manually gathering and synthesizing information scattered across repositories, documentation, technical blogs, and community discussions into a single coherent analysis.

The skill employs a structured four-round research methodology: Round 1 uses GitHub API to extract repository metrics, README content, file structures, contributors, commits, issues, pull requests, and releases. Round 2 performs discovery through web searches to identify key terms, official sources, and competitors. Round 3 conducts deep investigation with targeted searches and content fetching for technical architecture and community sentiment. Round 4 performs a deep dive into commit history, issue evolution, and contributor activity patterns. Each round builds on previous findings using a broad-to-narrow query strategy.

This skill is designed for developers, technical leaders, and researchers who need comprehensive analysis of open source projects for competitive research, technology evaluation, due diligence, or timeline reconstruction. It produces structured reports with confidence scoring, source categorization, Mermaid diagrams for visualizing architectures and timelines, and balanced assessments of strengths and weaknesses. Output reports follow a standardized template ensuring consistency and completeness across different research subjects.

FAQ

What types of GitHub repositories can this skill analyze?

This skill can analyze any public GitHub repository. It works best with repositories that have substantial activity, documentation, and community engagement. It extracts data including repository metrics, README files, file structures, contributors, commits, issues, pull requests, and releases.

How many research rounds does the skill perform and what does each round do?

The skill performs four research rounds: Round 1 uses GitHub API to extract repository data directly. Round 2 conducts 3-5 web searches for discovery and overview. Round 3 performs 5-10 web searches plus content fetching for deep investigation of architecture and sentiment. Round 4 does a deep dive into commit history, issues, and contributor patterns.

What format does the output report use?

The skill generates a structured markdown report saved as 'research_{topic}_{YYYYMMDD}.md'. Reports include metadata, executive summary, chronological timeline, key analysis sections, metrics tables, strengths/weaknesses assessment, categorized sources, confidence scoring, and Mermaid diagrams for visualizing timelines, architectures, and comparisons.

How does the skill assess confidence in its findings?

The skill assigns confidence levels based on source quality: High confidence (90%+) for official documentation, GitHub data, and multiple corroborating sources; Medium confidence (70-89%) for single reliable sources and recent articles; Low confidence (50-69%) for social media, unverified claims, and outdated information.

When should I use this skill instead of manual research?

Use this skill when you need comprehensive analysis of GitHub repositories, competitive comparisons, technology evaluation, timeline reconstruction, or in-depth investigation of open source projects. It's particularly valuable when you need structured reports combining multiple data sources, confidence assessments, and visual diagrams that would be time-consuming to create manually.