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Research
Skill

/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,

From plugin
deer-flow
80k29 skills3 agents
Install
$ npx -y skills add bytedance/deer-flow --skill github-deep-research --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/github-deep-research

Context preview

The summary Claude sees to decide when to auto-load this skill.

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,

SKILL.md

github-deep-research.SKILL.md
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.
Read more
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