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

# 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:

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

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

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 converter
│   ├── verify_html.py                # HTML verification
│   └── research_engine.py            # Core orchestration engine
└── tests/
    └── fixtures/                     # Test report fixtures

Version History

License

MIT - modify as needed for your workflow.