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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Enterprise-grade deep research skill for Claude Code with 8-phase pipeline, source credibility scoring, and automated validation. Outperforms OpenAI, Gemini, and Claude Desktop in quality and verification.
| Date | Stars |
|---|---|
| 2026-07-31 | 965 |
| 2026-08-03 | 967 |
| 2026-08-06 | 967 |
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# Deep Research Skill for Claude Code Enterprise-grade research engine for Claude Code. Produces citation-backed reports with source credibility scoring, multi-provider search, and automated validation. ## Installation ```bash # Clone into Claude Code skills directory git clone https://github.com/199-biotechnologies/claude-deep-research-skill.git ~/.claude/skills/deep-research ``` No additional dependencies required for basic usage. ### Optional: search-cli (multi-provider search) For aggregated search across Brave, Serper, Exa, Jina, and Firecrawl: ```bash brew tap 199-biotechnologies/tap && brew install search-cli search config set keys.brave YOUR_KEY # configure at least one provider ``` ## Usage ``` deep research on the current state of quantum computing ``` ``` deep research in ultradeep mode: compare PostgreSQL vs Supabase for our stack ``` ## Research Modes | Mode | Phases | Duration | Best For | |------|--------|----------|----------| | Quick | 3 | 2-5 min | Initial exploration | | Standard | 6 | 5-10 min | Most research questions | | Deep | 8 | 10-20 min | Complex topics, critical decisions | | UltraDeep | 8+ | 20-45 min | Comprehensive reports, maximum rigor | ## Pipeline Scope → Plan → **Retrieve** (parallel search + agents) → Triangulate → Outline Refinement → Synthesize → Critique (with loop-back) → Refine → Package Key features: - **Step 0**: Retrieves current date before searches (prevents stale training-data year assumptions) - **Parallel retrieval**: 5-10 concurrent searches + 2-3 focused sub-agents returning structured evidence objects - **First Finish Search**: Adaptive quality thresholds by mode - **Critique loop-back**: Phase 6 can return to Phase 3 with delta-queries if critical gaps found - **Multi-persona red teaming**: Skeptical Practitioner, Adversarial Reviewer, Implementation Engineer (Deep/UltraDeep) - **Disk-persisted citations**: `sources.json` survives context compaction and continuation agents ## Output Reports saved to `~/Documents/[Topic]_Research_[Date]/`: - Markdown (primary source of truth) - HTML (McKinsey-style, auto-opened in browser) - PDF (professional print via WeasyPrint) Reports >18K words auto-continue via recursive agent spawning with context preservation. ## Quality Standards - 10+ sources, 3+ per major claim - Executive summary 200-400 words - Findings 600-2,000 words each, prose-first (>=80%) - Full bibliography with URLs, no placeholders - Automated validation: `validate_report.py` (9 checks) + `verify_citations.py` (DOI/URL/hallucination detection) - Validation loop: validate → fix → retry (max 3 cycles) ## Search Tools | Tool | Priority | Setup | |------|----------|-------| | search-cli | **Primary** — all searches go here first | `brew install search-cli` + API keys | | WebSearch | Fallback — if search-cli fails or rate-limited | None (built-in) | | Exa MCP | Optional — semantic/neural search alongside search-cli | MCP config | ## Architecture ``` deep-research/ ├── SKILL.md # Skill entry point (lean, ~100 lines) ├── reference/ │ ├── methodology.md # 8-phase pipeline details │ ├── report-assembly.md # Progressive generation strategy │ ├── quality-gates.md # Validation standards │ ├── html-generation.md # McKinsey HTML conversion │ ├── continuation.md # Auto-continuation protocol │ └── weasyprint_guidelines.md # PDF generation ├── templates/ │ ├── report_template.md # Report structure template │ └── mckinsey_report_template.html # HTML report template ├── scripts/ │ ├── validate_report.py # 9-check structure validator │ ├── verify_citations.py # DOI/URL/hallucination checker │ ├── source_evaluator.py # Source credibility scoring │ ├── citation_manager.py # Citation tracking │ ├── md_to_html.py # Markdown to HTML con
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:07edead3666b97d7, desc:scoring