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GitHub Deep Research

Produces multi-round, cited research reports on a GitHub repository and its ecosystem

Source Download skill
Repository
bytedance/deer-flow
Stars
84,876
Licence
MIT
Files
3 files

What it does

When installed, the agent follows a structured research process on a GitHub project. It first runs a bundled Python script that pulls repository facts from the GitHub API (summary, README, file tree, commits, issues, pull requests, releases, contributors), then runs several web searches and page fetches for architecture, competitors, community discussion and roadmap. It then writes a markdown report with an executive summary, timeline, metrics tables, Mermaid diagrams, confidence ratings and inline source citations.

Great for

  • Developers evaluating whether an open-source project is worth adopting
  • Analysts or students who need a structured report on a repository's history and competitors
  • Anyone who wants a timeline of a project reconstructed from commits and issues

Skip it if

  • Quick factual lookups, since the multi-round process is slow and produces long reports
  • Private repositories, because the script calls the public GitHub API without any credentials in the excerpt shown, so access will fail or be limited
  • Decisions requiring verified facts without review, since web sources can be outdated or biased and the skill itself warns about this

Try asking

  • “Do a deep research report on the github.com/bytedance/deer-flow repository, including its timeline and architecture”
  • “Compare this open-source agent framework with its alternatives and cite sources”
  • “Summarise the recent issues, PRs and releases of owner/repo and flag the roadmap”
  • “Produce a research report on this project's contributor activity and community sentiment”
Step-by-step guide: how to use GitHub Deep ResearchInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 3 original files, the MIT licence and an attribution note.

    github-deep-research.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip github-deep-research.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip github-deep-research.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Give a full GitHub URL or owner/repo name so the script receives the correct arguments
  • Ask for specific sections (timeline, competitors, metrics) to keep the report focused
  • Review the cited sources and confidence labels before relying on any claim

What's inside

  • assets/report_template.md
  • scripts/github_api.py
  • SKILL.md

Redistributed unmodified from bytedance/deer-flow under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.