Deep Research Skill for Claude Code

A comprehensive research engine that brings Claude Desktop's Advanced Research capabilities (and more) to Claude Code terminal.

Features

Core Research Pipeline

• 8.5-Phase Research Pipeline: Scope → Plan → Retrieve (Parallel) → Triangulate → Outline Refinement → Synthesize → Critique → Refine → Package
• Multiple Research Modes: Quick, Standard, Deep, and UltraDeep
• Graph-of-Thoughts Reasoning: Non-linear exploration with branching thought paths

2025 Enhancements (Latest - v2.2)

• 🔄 Auto-Continuation System (NEW): TRUE UNLIMITED length (50K, 100K+ words) via recursive agent spawning with context preservation
• 📄 Progressive File Assembly: Section-by-section generation with quality safeguards
• ⚡ Parallel Search Execution: 5-10 concurrent searches + parallel agents (3-5x faster Phase 3)
• 🎯 First Finish Search (FFS) Pattern: Adaptive completion based on quality thresholds
• 🔍 Enhanced Citation Validation (CiteGuard): Hallucination detection, URL verification, multi-source cross-checking
• 📋 Dynamic Outline Evolution (WebWeaver): Adapt structure after Phase 4 based on evidence
• 🔗 Attribution Gradients UI: Interactive citation tooltips showing evidence chains in HTML reports
• 🛡️ Anti-Fatigue Enforcement: Prose-first quality checks prevent bullet-point degradation

Traditional Strengths

• Citation Management: Automatic source tracking and bibliography generation
• Source Credibility Assessment: Evaluates source quality and potential biases
• Structured Reports: Professional markdown, HTML (McKinsey-style), and PDF outputs
• Verification & Triangulation: Cross-references claims across multiple sources

Installation

The skill is already installed globally in ~/.claude/skills/deep-research/

No additional dependencies required for basic usage.

Usage

In Claude Code

Simply invoke the skill:

Use deep research to analyze the state of quantum computing in 2025

Or specify a mode:

Use deep research in ultradeep mode to compare PostgreSQL vs Supabase

Direct CLI Usage

# Standard research
python ~/.claude/skills/deep-research/research_engine.py --query "Your research question" --mode standard

# Deep research (all 8 phases)
python ~/.claude/skills/deep-research/research_engine.py --query "Your research question" --mode deep

# Quick research (3 phases only)
python ~/.claude/skills/deep-research/research_engine.py --query "Your research question" --mode quick

# Ultra-deep research (extended iterations)
python ~/.claude/skills/deep-research/research_engine.py --query "Your research question" --mode ultradeep

Research Modes

Mode	Phases	Duration	Best For
Quick	3 phases	2-5 min	Simple topics, initial exploration
Standard	6 phases	5-10 min	Most research questions
Deep	8 phases	10-20 min	Complex topics requiring thorough analysis
UltraDeep	8+ phases	20-45 min	Critical decisions, comprehensive reports

Output

Research reports are saved to organized folders in ~/Documents/[Topic]_Research_[Date]/

Each report includes:

• Executive Summary
• Detailed Analysis with Citations
• Synthesis & Insights
• Limitations & Caveats
• Recommendations
• Full Bibliography
• Methodology Appendix

Unlimited Report Generation (2025 Auto-Continuation System)

Reports use progressive file assembly with auto-continuation - achieving truly unlimited length through recursive agent spawning:

How It Works:

1. Initial Generation (18K words)

   • Generate sections 1-10 progressively
   • Each section written to file immediately (stays under 32K limit per agent)
   • Save continuation state with research context

2. Auto-Continuation (if needed)

   • Automatically spawns continuation agent via Task tool
   • Continuation agent loads state: themes, narrative arc, citations, quality metrics
   • Generates next batch of sections (another 18K words)
   • Updates state and spawns next agent if more sections remain

3. Recursive Chaining

   • Each agent stays under 32K output token limit
   • Chain continues until all sections complete
   • Final agent generates bibliography and validates report

Realistic Report Sizes:

• Quick mode: 2,000-4,000 words (single run) ✅
• Standard mode: 4,000-8,000 words (single run) ✅
• Deep mode: 8,000-15,000 words (single run) ✅
• UltraDeep mode: 20,000-100,000+ words (auto-continuation) ✅

Example: 50,000 word report:

• Agent 1: Sections 1-10 (18K words) → Spawns Agent 2
• Agent 2: Sections 11-20 (18K words) → Spawns Agent 3
• Agent 3: Sections 21-25 + Bibliography (14K words) → Complete!
• Total: 50K words across 3 agents, each under 32K limit

Context Preservation (Quality Safeguards):

Continuation state includes:

• ✅ Research question and key themes
• ✅ Main findings summaries (100 words each)
• ✅ Narrative arc position (beginning/middle/end)
• ✅ Quality metrics (avg words, citation density, prose ratio)
• ✅ All citations used + bibliography entries
• ✅ Writing style characteristics

Each continuation agent:

• Reads last 3 sections to understand flow
• Maintains established themes and style
• Continues citation numbering correctly
• Matches quality metrics (±20% tolerance)
• Verifies coherence before each section

Quality Gates (Per Section):

• [ ] Word count: Within ±20% of average
• [ ] Citation density: Matches established rate
• [ ] Prose ratio: ≥80% prose (not bullets)
• [ ] Theme alignment: Ties to key themes
• [ ] Style consistency: Matches established patterns

Benefits:

• ✅ TRUE unlimited length (50K, 100K+ words achievable)
• ✅ Fully automatic (no manual intervention)
• ✅ Context preserved across continuations
• ✅ Quality maintained throughout
• ✅ Each agent stays under 32K token limit
• ✅ Progressive assembly prevents truncation

Examples

Technology Analysis

Use deep research to evaluate whether we should adopt Next.js 15 for our project

Market Research

Use deep research to analyze longevity biotech funding trends 2023-2025

Technical Decision

Use deep research to compare authentication solutions: Auth0 vs Clerk vs Supabase Auth

Scientific Review

Use deep research in ultradeep mode to summarize recent advances in senolytic therapies

Quality Standards

Every research output:

• ✅ Minimum 10+ distinct sources
• ✅ Citations for all major claims
• ✅ Cross-verified facts (3+ sources)
• ✅ Executive summary under 250 words
• ✅ Limitations section
• ✅ Full bibliography
• ✅ Methodology documentation

Architecture

deep-research/
├── SKILL.md                    # Main skill definition
├── research_engine.py          # Core orchestration engine
├── utils/
│   ├── citation_manager.py    # Citation tracking & bibliography
│   └── source_evaluator.py    # Source credibility assessment
├── requirements.txt
└── README.md

Tips for Best Results

1. Be Specific: Frame questions clearly with context
2. Set Expectations: Specify if you need comparisons, recommendations, or pure analysis
3. Choose Appropriate Mode: Use Quick for exploration, Deep for decisions
4. Review Scope: Check Phase 1 output to ensure research is on track
5. Leverage Citations: Use citation numbers to drill deeper into specific sources

Comparison with Claude Desktop Research

Feature	Claude Desktop	Deep Research Skill
Multi-source synthesis	✅	✅
Citation tracking	✅	✅
Iterative refinement	✅	✅
Source verification	✅	✅ Enhanced
Credibility scoring	❌	✅
8-phase methodology	❌	✅
Graph-of-Thoughts	❌	✅
Multiple modes	❌	✅
Local file integration	❌	✅
Code execution	❌	✅

2025 Research Papers Implemented

This skill now incorporates cutting-edge techniques from 2025 academic research:

1. Parallel Execution (GAP, Flash-Searcher, TPS-Bench)

   • DAG-based parallel tool use for independent subtasks
   • 3-5x faster retrieval phase
   • Concurrent search strategies

2. First Finish Search (arXiv 2505.18149)

   • Quality threshold gates by mode
   • Continue background searches for depth
   • Optimal latency-accuracy tradeoff

3. Citation Validation (CiteGuard, arXiv 2510.17853)

   • Hallucination pattern detection
   • Multi-source verification (DOI + URL)
   • Strict mode for critical reports

4. Dynamic Outlines (WebWeaver, arXiv 2509.13312)

   • Evidence-driven structure adaptation
   • Phase 4.5 refinement step
   • Prevents locked-in research paths

5. Attribution Gradients (arXiv 2510.00361)

   • Interactive evidence chains
   • Hover tooltips in HTML reports
   • Improved auditability

Version

2.0 (2025-11-05) - Major update with 2025 research enhancements
1.0 (2025-11-04) - Initial release

License

User skill - modify as needed for your workflow