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Github Deep Research

  • 2.2k installs
  • 78k repo stars
  • Updated July 27, 2026
  • bytedance/deer-flow

github-deep-research is an agent skill that Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruc.

About

Multi round research combining GitHub API web_search web_fetch to produce comprehensive markdown reports Round 1 GitHub API Round 2 Discovery Round 3 Deep Investigation Round 4 Deep Dive 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 The github deep research agent skill provides documented workflows prerequisites triggers and safety guidance from its SKILL md source Agents load it when user requests match the description and follow step by step instructions without inventing capabilities It integrates with standard agent tooling for the tasks inputs outputs and failure modes described in the repository documentation

  • description: Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeli
  • Multi-round research combining GitHub API, web_search, web_fetch to produce comprehensive markdown reports.
  • **Broad to Narrow**: Start with GitHub API, then general queries, refine based on findings.
  • Follow github-deep-research SKILL.md steps and documented constraints.
  • Follow github-deep-research SKILL.md steps and documented constraints.

Github Deep Research by the numbers

  • 2,242 all-time installs (skills.sh)
  • +84 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Ranked #405 of 16,659 AI & Agent Building skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
At a glance

github-deep-research capabilities & compatibility

Capabilities
description: conduct multi round deep research o · multi round research combining github api, web_s · **broad to narrow**: start with github api, then · follow github deep research skill.md steps and d
Use cases
orchestration
From the docs

What github-deep-research says it does

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. Pr
SKILL.md
Multi-round research combining GitHub API, web_search, web_fetch to produce comprehensive markdown reports.
SKILL.md
**Broad to Narrow**: Start with GitHub API, then general queries, refine based on findings.
SKILL.md
npx skills add https://github.com/bytedance/deer-flow --skill github-deep-research

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Listed on Skillselion
Installs2.2k
repo stars78k
Security audit2 / 3 scanners passed
Last updatedJuly 27, 2026
Repositorybytedance/deer-flow

When should an agent use github-deep-research and what problem does it solve?

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 struct

Who is it for?

Developers invoking github-deep-research as documented in the skill source.

Skip if: Skip when requirements fall outside github-deep-research documented scope.

When should I use 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 struct

What you get

Outputs aligned with the github-deep-research SKILL.md workflow and stated deliverables.

  • Citation-backed repository research report
  • Executive summary with confidence metadata

By the numbers

  • Report template includes 10+ repository metadata fields including stars, forks, open issues, and languages

Files

SKILL.mdMarkdownGitHub ↗

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:

ConfidenceCriteria
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.

Related skills

How it compares

Pick github-deep-research over generic web search when you need a single structured, citation-backed dossier on one GitHub repository.

FAQ

What is 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 Git

When should I use 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 Git

Is github-deep-research safe to install?

Review the Security Audits panel on this page before production use.

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