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Deep Research

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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.

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  • Added September 25, 2026
ai-agentspythondebugging

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Scanned September 25, 2026

npx -y skills add nicholyx/ai-skills --skill deep-research --agent claude-code

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SKILL.md
---
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

| 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.

Files in this skill

  • .gitignore264 B
  • README.md4.8 KB
  • SKILL.md4.2 KB
  • reference/continuation.md4.5 KB
  • reference/html-generation.md2.9 KB
  • reference/methodology.md16.1 KB
  • reference/quality-gates.md5.7 KB
  • reference/report-assembly.md3.9 KB
  • reference/weasyprint_guidelines.md5.7 KB
  • requirements.txt579 B
  • schemas/claim.schema.json1.6 KB
  • schemas/evidence.schema.json1.4 KB
  • schemas/run_manifest.schema.json2.8 KB
  • schemas/source.schema.json1.6 KB
  • scripts/citation_manager.py10.1 KB
  • scripts/evidence_store.py6.4 KB
  • scripts/extract_claims.py11.8 KB
  • scripts/md_to_html.py9.7 KB
  • scripts/research_engine.py16.4 KB
  • scripts/source_evaluator.py9.3 KB

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