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.
What this skill does
# 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()`:
```bash
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)**:
```mermaid
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)**:
```mermaid
flowchart TD
A[User] --> B[Coordinator]
B --> C[Planner]
C --> D[Research Team]
D --> E[Reporter]
```
**Comparison (Pie/Bar)**:
```mermaid
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
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:**
```markdown
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:**
```markdown
The project gained 10,000 stars within 3 months of launch.
The architecture uses LangGraph for workflow orchestration.
```
Related in Writing & Docs
jax-development
IncludedUse this skill when the user is writing, debugging, profiling, refactoring, reviewing, benchmarking, parallelising, exporting, or explaining JAX code, or when they mention JAX, jax.numpy, jit, grad, value_and_grad, vmap, scan, lax, random keys, pytrees, jax.Array, sharding, Mesh, PartitionSpec, NamedSharding, pmap, shard_map, Pallas, XLA, StableHLO, checkify, profiler, or the JAX repo. It helps turn NumPy or PyTorch-style code into pure functional JAX, fix tracer/control-flow/shape/PRNG bugs, remove recompiles and host-device syncs, choose transforms and sharding strategies, inspect jaxpr/lowering/IR, and benchmark compiled code correctly.
nature-article-writer
IncludedDrafts, rewrites, diagnostically critiques, and style-calibrates primary research manuscripts for Nature and Nature Portfolio journals. Use when the user wants a Nature-style title, summary paragraph or abstract, introduction, results, discussion, methods, figure legends, presubmission enquiry, cover letter, reviewer response, or when a scientific draft sounds generic, jargon-heavy, structurally weak, or AI-ish and needs precise, broad-reader-friendly prose without inventing data, analyses, or references. Best for primary research articles and letters rather than reviews or press releases unless explicitly adapting one.
deckrd
IncludedDocument-driven framework that derives requirements, specifications, implementation plans, and executable tasks from goals through structured AI dialogue. Use when user says "write requirements", "create spec", "plan implementation", "derive tasks", "structure this feature", "break down into tasks", or "document this module". Also use for reverse engineering existing code into docs (/deckrd rev). Do NOT use for direct code writing — use /deckrd-coder after tasks are generated. Do NOT use when the user only wants to run or fix existing code without planning.
clinical-decision-support
IncludedGenerate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
handling-sf-data
IncludedSalesforce data operations with 130-point scoring. Use this skill to create, update, delete, bulk import/export, generate test data, and clean up org records using sf CLI and anonymous Apex. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, needs data factory patterns for Apex tests, or needs to seed/clean records in a Salesforce org. DO NOT TRIGGER when: SOQL query writing only (use querying-soql), Apex test execution (use running-apex-tests), or metadata deployment (use deploying-metadata).
accelint-ac-to-playwright
IncludedConvert and validate acceptance criteria for Playwright test automation. Use when user asks to (1) review/evaluate/check if AC are ready for automation, (2) assess if AC can be converted as-is, (3) validate AC quality for Playwright, (4) turn AC into tests, (5) generate tests from acceptance criteria, (6) convert .md bullets or .feature Gherkin files to Playwright specs, (7) create test automation from requirements. Handles both bullet-style markdown and Gherkin syntax with JSON test plan generation and validation.