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 que

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 Default assumptions: Technical query = technical audience. Comparison = balanced perspective. Trend = recent 1 2 years. Workflow Overview Phase Name Quick Std Deep Ultra 1 SCOPE Y Y Y Y 2 PLAN Y Y Y 3 RETRIEVE Y Y Y Y 4 TRIANGULATE Y Y Y 4.5 OUTLINE REFINEMENT Y Y Y 5 SYNTHESIZE Y Y Y 6 CRITIQUE Y Y 7 REFINE Y Y 8 PACKAGE Y Y Y Y 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](./reference/methodology.md) for detailed phase instructions 2. Phase 8 (Report): Load [report assembly.md](./reference/report assembly.md) for progressive generation 3. HTML/PDF output: Load [html generation.md](./reference/html generation.md) 4. Quality checks: Load [quality gates.md](./reference/quality gates.md) 5. Long reports ( 18K words): Load [continuation.md](./reference/continuation.md) Templates: Report structure: [report template.md](./templates/report template.md) HTML styling: [mckinsey report template.html](./templates/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.

deep-research