Agent Skillsbytedance/deer-flow › github-deep-research

github-deep-research

GitHub

针对GitHub仓库进行多轮深度研究,结合API与网络搜索生成包含摘要、时间线、架构图及指标分析的Markdown报告。

skills/public/github-deep-research/SKILL.md bytedance/deer-flow

Trigger Scenarios

用户请求对GitHub仓库进行全面分析 需要重构项目时间线或进行竞品对比 输入包含GitHub仓库URL或开源项目名称

Install

npx skills add bytedance/deer-flow --skill github-deep-research -g -y
More Options

Non-standard path

npx skills add https://github.com/bytedance/deer-flow/tree/main/skills/public/github-deep-research -g -y

Use without installing

npx skills use bytedance/deer-flow@github-deep-research

指定 Agent (Claude Code)

npx skills add bytedance/deer-flow --skill github-deep-research -a claude-code -g -y

安装 repo 全部 skill

npx skills add bytedance/deer-flow --all -g -y

预览 repo 内 skill

npx skills add bytedance/deer-flow --list

SKILL.md

Frontmatter
{
    "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."
}

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.

Version History

  • a5acc25 Current 2026-08-20 17:00

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Metadata

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