G

github-deep-research

Verified

by bytedance · Updated Apr 16, 2026

opus
1.2Kin7.7Kout
$0.783m 50s

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.

githubresearchrepository-analysisreportingopen-sourcemarkdown

See It In Action

1 turns·claude
The project codebase is in your current working directory. Explore the files to understand the code before making changes. need a quick but solid due diligence readout on LangGraph for leadership before this afternoon's partner call

I'll kick off deep research on LangGraph to get you a solid due diligence readout.

Starting the research. I'll run multiple rounds of data gathering.

Round 1: GitHub API data collection

Let me find the script and also start gathering data via gh CLI and web searches in parallel.

Good data from Round 1 and 2. Let me do deeper investigation on commercial model, adoption, and red flags.

Let me grab the commit velocity and the security vulnerability details.

Now I have all the data. Let me compile the report.

Done. The full report is saved at research_langgraph_20260413.md. Here's the quick takeaway for your call:


LangGraph in 30 seconds:

  • What: Graph-based agent orchestration framework from LangChain Inc. ($1.25B unicorn, $260M raised, backed by Sequoia/Benchmark/IVP)
  • Traction: 29K GitHub stars, MIT-licensed, ~600-800 companies in production (LinkedIn, Uber, Elastic, JPMorgan, Klarna)
  • Strengths: Best-in-class stateful orchestration, human-in-the-loop, model-agnostic, most mature open-source agent framework
  • Watch-outs:
    • Security: 4 CVEs in the past year including a critical RCE (all patched, but pattern warrants scrutiny)
    • Lock-in: Core is MIT, but commercial LangSmith/Platform create gravitational pull
    • Complexity: Steep learning curve; overkill for simple agent patterns where direct provider SDKs suffice
    • Contributor concentration: Top 3 contributors are all LangChain employees

Bottom line: Strong choice for complex stateful agents, but evaluate whether your use cases actually need the graph abstraction vs. simpler alternatives. If you proceed, pin versions, self-host where possible, and have an exit strategy.

Claude Sonnet 4.6

External Tools

ToolType
githubmcp
python3binary

Permissions

ScopeDescription
filesystem:write
network:outbound
process:spawn

SKILL.md

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.

FAQ

What does github-deep-research do?

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.

When should I use github-deep-research?

Use it when you need a repeatable workflow that produces text report.

What does github-deep-research output?

In the evaluated run it produced text report.

How do I install or invoke github-deep-research?

Ask the agent to use this skill when the task matches its documented workflow.

Which agents does github-deep-research support?

Agent support is inferred from the source, but not explicitly declared.

What tools, channels, or permissions does github-deep-research need?

It uses github, python3; channels commonly include text; permissions include filesystem:write, network:outbound, process:spawn.

Is github-deep-research safe to install?

Static analysis marked this skill as medium risk; review side effects and permissions before enabling it.

How is github-deep-research different from an MCP or plugin?

A skill packages instructions and workflow conventions; tools, MCP servers, and plugins are dependencies the skill may call during execution.

Does github-deep-research outperform not using a skill?

About github-deep-research

When to use github-deep-research

You need a comprehensive investigation of a GitHub repository or open source project. You want a timeline, metrics summary, and competitive or architectural analysis of a repo. You need a citation-backed markdown research report about a GitHub project.

When github-deep-research is not the right choice

You only need a quick repo summary or basic file inspection. You need to analyze non-GitHub sources without repository-centric research.

What it produces

Produces text report.