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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, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source projects.

DeepseekModel Curated skill Quality Excellent · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=bytedance-deer-flow-skills-public-github-deep-research-skill-md&format=skill
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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 : Official docs/repos (highest weight) Technical blogs (Medium, Dev.to) News articles (verified outlets) Community discussions (Reddit, HN) 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 : Metadata Block - Date, confidence level, subject Executive Summary - 2-3 sentence overview with key metrics Chronological Timeline - Phased breakdown with dates Key Analysis Sections - Topic-specific deep dives Metrics & Comparisons - Tables, growth charts Strengths & Weaknesses - Balanced assessment Sources - Categorized references Confidence Assessment - Claims by confidence level 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 Start with official sources - Repo, docs, company blog Verify dates from commits/PRs - More reliable than articles Triangulate claims - 2+ independent sources Note conflicting info - Don't hide contradictions Distinguish fact vs opinion - Label speculation clearly CRITICAL: Always include inline citations - Use [citation:Title](URL) format immediately after each claim from external sources Extract URLs from search results - web_search returns {title, url, snippet} - always use the URL field 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.
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The downloaded .skill package contains the following fields.
Field Description
formatFormat tag (skill/v1)
skill_idUnique skill ID
nameSkill name
versionVersion
descriptionDescription
categoryCategories (array)
trigger_wordsTrigger words
tagsTags
sourceSource
source_urlSource URL (this page)
exported_atExported at (set per download)
system_promptSystem prompt body
model_configModel config: provider / model / temperature / max_tokens / top_p
examplesExamples
install_guideImport guide for Coze / Dify / Claude / custom frameworks
The same skill can be exported in different platform formats.
.skill Standard format with system_prompt and model_config, ready for any agent framework Download
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Coze Markdown with frontmatter, for Coze platform import Download
Dify Dify DSL, import directly after creating an app Download

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