GitHub Deep Research

What it does

When your agent uses it

• Investigating the history and direction of an open source project
• Comparing a repository with its competitors
• Reconstructing a project timeline from commits, issues and pull requests

Example prompts

• “Research the BurntSushi/ripgrep repository in depth and give me a timeline of key releases.”
• “Compare this project with its main alternatives using the GitHub API and web sources.”
• “Reconstruct how the feature set of this repo evolved from its commits and issues.”

Requirements

• Python to run scripts/github_api.py
• Web search and fetch tools
• Network access to the GitHub API

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Official docs/repos (highest weight)
2. 2Technical blogs (Medium, Dev.to)
3. 3News articles (verified outlets)
4. 4Community discussions (Reddit, HN)
5. 5Social media (lowest weight, for sentiment)

What it can do on your machine

Read from SKILL.md and the folder at commit 35cdcab. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 1 file in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • github.com
  • langchain.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

GitHub Deep Research loads about 1.3k tokens when it runs. Until then it costs about 98 tokens; SKILL.md has 403 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~98

When it runs · the whole SKILL.md, loaded when a task matches

~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from bytedance/deer-flow at commit 35cdcab, republished under its MIT licence (© bytedance). 403 words, ~1,260 tokens.

name

github-deep-research

description

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.

Round 1: GitHub API
Round 2: "{topic} overview"
Round 3: "{topic} architecture", "{topic} vs alternatives"
Round 4: "{topic} issues", "{topic} roadmap", "site:github.com {topic}"

python /path/to/skill/scripts/github_api.py <owner> <repo> summary
python /path/to/skill/scripts/github_api.py <owner> <repo> readme
python /path/to/skill/scripts/github_api.py <owner> <repo> tree

gantt
    title Project Timeline
    dateFormat YYYY-MM-DD
    section Phase 1
    Development    :2025-01-01, 2025-03-01
    section Phase 2
    Launch         :2025-03-01, 2025-04-01

flowchart TD
    A[User] --> B[Coordinator]
    B --> C[Planner]
    C --> D[Research Team]
    D --> E[Reporter]

pie title Market Share
    "Project A" : 45
    "Project B" : 30
    "Others" : 25

The project gained 10,000 stars within 3 months of launch [citation:GitHub Stats](https://github.com/owner/repo).
The architecture uses LangGraph for workflow orchestration [citation:LangGraph Docs](https://langchain.com/langgraph).

The project gained 10,000 stars within 3 months of launch.
The architecture uses LangGraph for workflow orchestration.

© bytedance, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts, assets) in skills/public/github-deep-research of bytedance/deer-flow.

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

Open the folder on GitHubat commit 35cdcab

Used in 5 other repositories

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 5 other GitHub owners. This page covers the copy in bytedance/deer-flow, which our catalogue first saw on October 7, 2026.

• ljxpython/ai-agent-platformapps/runtime-service/src/runtime_service/services/dearflow_agent/skills/github-deep-research
• MagicCube/agentarauser-home/.claude/skills/github-deep-research
• stophobia/deerflow2.0-enhancedskills/public/github-deep-research
• Citrus-bit/Anaxaskills/public/github-deep-research
• majiayu000/claude-skill-registryskills/analysis/github-deep-research