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/github-deep-research

@9809af1
by Bytedance Inc.bytedance/deer-flow83k stars
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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.

Use this Skill: https://skilld.dev/gh/bytedance/deer-flow/github-deep-research

This session only. Nothing lands on disk.

SKILL.md

≈98 tokens always: the name and description. ≈1.2k when used: this file. ≈895 more on demand in 1 file.

GitHub Deep Research Skill

Multi-round research combining GitHub API, web_search, web_fetch to produce comprehensive markdown reports.

Research Workflow

  • Round 1: GitHub API
  • Round 2: Discovery
  • Round 3: Deep Investigation
  • Round 4: Deep Dive

Core Methodology

Query Strategy

Broad to Narrow: Start with GitHub API, then general queries, refine based on findings.

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}"

Source Prioritization:

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

Research Rounds

Round 1 - GitHub API Directly execute scripts/github_api.py without read_file():

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

Available commands (the last argument of github_api.py):

  • summary
  • info
  • readme
  • tree
  • languages
  • contributors
  • commits
  • issues
  • prs
  • releases

Round 2 - Discovery (3-5 web_search)

  • Get overview and identify key terms
  • Find official website/repo
  • Identify main players/competitors

Round 3 - Deep Investigation (5-10 web_search + web_fetch)

  • Technical architecture details
  • Timeline of key events
  • Community sentiment
  • Use web_fetch on valuable URLs for full content

Round 4 - Deep Dive

  • Analyze commit history for timeline
  • Review issues/PRs for feature evolution
  • Check contributor activity

Report Structure

Follow template in assets/report_template.md:

  1. Metadata Block - Date, confidence level, subject
  2. Executive Summary - 2-3 sentence overview with key metrics
  3. Chronological Timeline - Phased breakdown with dates
  4. Key Analysis Sections - Topic-specific deep dives
  5. Metrics & Comparisons - Tables, growth charts
  6. Strengths & Weaknesses - Balanced assessment
  7. Sources - Categorized references
  8. Confidence Assessment - Claims by confidence level
  9. Methodology - Research approach used

Mermaid Diagrams

Include diagrams where helpful:

Timeline (Gantt):

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

Architecture (Flowchart):

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

Comparison (Pie/Bar):

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

Confidence Scoring

Assign confidence based on source quality:

Confidence Criteria
High (90%+) Official docs, GitHub data, multiple corroborating sources
Medium (70-89%) Single reliable source, recent articles
Low (50-69%) Social media, unverified claims, outdated info

Output

Save report as: research_{topic}_{YYYYMMDD}.md

Formatting Rules

  • Chinese content: Use full-width punctuation(,。:;!?)
  • Technical terms: Provide Wiki/doc URL on first mention
  • Tables: Use for metrics, comparisons
  • Code blocks: For technical examples
  • Mermaid: For architecture, timelines, flows

Best Practices

  1. Start with official sources - Repo, docs, company blog
  2. Verify dates from commits/PRs - More reliable than articles
  3. Triangulate claims - 2+ independent sources
  4. Note conflicting info - Don't hide contradictions
  5. Distinguish fact vs opinion - Label speculation clearly
  6. CRITICAL: Always include inline citations - Use [citation:Title](URL) format immediately after each claim from external sources
  7. Extract URLs from search results - web_search returns {title, url, snippet} - always use the URL field
  8. Update as you go - Don't wait until end to synthesize

Citation Examples

Good - With inline citations:

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).

Bad - Without citations:

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

Source: SKILL.md on GitHub

2 warnings16d5 checks · Risk SAFE
  • Gen Agent Trust Hub16d

    The skill is a professional research tool designed to analyze GitHub repositories using official APIs and standard web investigation methods. It contains a self-contained Python script for API interactions and follows security best practices for credential handling.

  • Socket16d

    No alerts

  • Snyk16d

    Risk: MEDIUM · 1 issue

  • Runlayer7mo

    3/3 files flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at 9809af1. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 7 months ago
  • API
  • github
  • research
  • web-search
  • analysis
  • markdown
  • reporting
  • competitive-analysis
  • timeline

README badge

README badge for bytedance/deer-flow/github-deep-research

Conducts multi-round research on GitHub repositories using GitHub API, web search, and web fetch to produce structured markdown reports with timelines, metrics, and architecture diagrams. Targets comprehensive analysis workflows such as competitive analysis, project history reconstruction, and open source investigation.

Generated from the current SKILL.md.

What sources does this skill use to conduct research?
The skill combines GitHub API data, web searches, and web fetches, prioritizing official docs and repos over social media. It follows a four-round methodology: GitHub API queries first, then broad discovery, deep investigation, and finally a deep dive into commit history and issues.
Can this skill analyze private repositories?
The SKILL.md does not specify whether private repos are supported. GitHub API access depends on authentication and permissions, which are not detailed in the skill documentation.
What format are the research reports in?
Reports are structured markdown files with an executive summary, chronological timeline, metrics analysis, Mermaid diagrams, and confidence-scored claims. The output file is saved as `research_{topic}_{YYYYMMDD}.md`.
Does this skill verify the accuracy of claims it finds?
Yes. The skill requires triangulating claims across 2+ independent sources, distinguishing fact from opinion, and assigning confidence scores (High 90%+, Medium 70-89%, Low 50-69%) based on source quality and corroboration.

Generated from the current SKILL.md. These answers refresh after source changes.