deep-research

name: deep-research description: Use when the user needs multi-source research with citation tracking, evidence persistence, and structured report generation. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1-2 searches.

Deep Research

Core Purpose

Deliver citation-tracked research reports through a structured pipeline with evidence persistence, source identity management, claim-level verification, and progressive context management.

Autonomy Principle: Operate independently. Infer assumptions from context. Only stop for critical errors or incomprehensible queries. Surface high-materiality assumptions explicitly in the Introduction and Methodology rather than silently defaulting.

Decision Tree

Request Analysis
+-- Simple lookup? --> STOP: Use WebSearch
+-- Debugging? --> STOP: Use standard tools
+-- Complex analysis needed? --> CONTINUE

Mode Selection
+-- Initial exploration --> quick (3 phases, 2-5 min)
+-- Standard research --> standard (6 phases, 5-10 min) [DEFAULT]
+-- Critical decision --> deep (8 phases, 10-20 min)
+-- Comprehensive review --> ultradeep (8+ phases, 20-45 min)

Default assumptions: Technical query = technical audience. Comparison = balanced perspective. Trend = recent 1-2 years.

Workflow Overview

Note: Phases 3-5 operate as an evidence loop per section (retrieve → evidence store → refine outline → draft → verify claims → delta-retrieve if needed), not as strict sequential gates.

Execution

On invocation, load relevant reference files:

1. Phase 1-7: Load methodology.md for detailed phase instructions
2. Phase 8 (Report): Load report-assembly.md for progressive generation
3. HTML/PDF output: Load html-generation.md
4. Quality checks: Load quality-gates.md
5. Long reports (>18K words): Load continuation.md

Templates:

• Report structure: report_template.md
• HTML styling: mckinsey_report_template.html

Scripts:

• python scripts/validate_report.py --report [path]
• python scripts/verify_citations.py --report [path]
• python scripts/md_to_html.py [markdown_path]

Output Contract

Required sections:

• Executive Summary (200-400 words)
• Introduction (scope, methodology, assumptions)
• Main Analysis (4-8 findings, 600-2,000 words each, cited)
• Synthesis & Insights (patterns, implications)
• Limitations & Caveats
• Recommendations
• Bibliography (COMPLETE - every citation, no placeholders)
• Methodology Appendix

Output files (all to ~/Documents/[Topic]_Research_[YYYYMMDD]/):

• Markdown (primary source of truth)
• sources.jsonl — stable source registry with canonical IDs
• evidence.jsonl — append-only evidence store with quotes and locators
• claims.jsonl — atomic claim ledger with support status
• run_manifest.json — query, mode, assumptions, provider config
• HTML (McKinsey style, auto-opened)
• PDF (professional print, auto-opened)

Quality standards:

• 10+ sources, 3+ per major claim (cluster-independent, not just count)
• All factual claims cited immediately [N] with evidence backing in evidence.jsonl
• Claim-support verification mandatory: no unsupported factual claims pass delivery
• No placeholders, no fabricated citations
• Prose-first (>=80%), bullets sparingly

When to Use / NOT Use

Use: Comprehensive analysis, technology comparisons, state-of-the-art reviews, multi-perspective investigation, market analysis.

Do NOT use: Simple lookups, debugging, 1-2 search answers, quick time-sensitive queries.