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
官方收录技能
质量 优秀 · 90
v1.0.0
获取
https://deepseekmodel.com/api/download.php?id=bytedance-deer-flow-skills-public-github-deep-research-skill-md&format=skill
下载 .skill
标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
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.
Agent 识别该技能的关键词,点击任意一个即可复制。
该技能未提供触发词。
下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识(skill/v1) |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类(数组) |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接(本页地址) |
| exported_at | 导出时间(每次下载生成) |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数:provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明(Coze / Dify / Claude / 自定义框架) |