academic-paper-review
Use this skill when the user requests to review, analyze, critique, or summarize academic…
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,
$ npx -y skills add bytedance/deer-flow --skill github-deep-research --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/github-deep-researchContext 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,
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.
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.
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)
**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`):**
**Round 2 - Discovery (3-5 web_search)**
**Round 3 - Deep Investigation (5-10 web_search + web_fetch)**
**Round 4 - Deep Dive**
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
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" : 25Assign 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 |
Save report as: `research_{topic}_{YYYYMMDD}.md`
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
**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.
On February 28th, 2026, DeerFlow claimed the 🏆 #1 spot on GitHub Trending following the launch of version 2. Thanks a million to our incredible community — you made this happen!
Repo: bytedance/deer-flow
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