deep-research · v1.0 · 2026-09-05 · sha256 5178e9211777986c

deep-research v1.0A

Immutable. This exact content never changes and is served at /api/v1/blob/5178e9211777986c.

---
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.
version: "1.0"
updated: "2026-09-04"
---

# Deep Research

## Workflow nativo vs esta skill

O Claude Code tem um workflow bundled `/deep-research` que faz fan-out de web searches, cross-check de fontes e entrega um relatório citado. **Use o workflow nativo quando:**
- A pergunta é factual e baseada em web (notícias, documentação pública, comparações rápidas)
- Velocidade importa mais que rigor
- Não precisa de persistência estruturada (evidence.jsonl, claims.jsonl)

**Use esta skill quando:**
- Precisa de rastreamento de evidências persistido em disco (`evidence.jsonl`, `claims.jsonl`)
- Precisa de verificação de claims no nível atômico com ledger auditável
- Relatório final em formato McKinsey (HTML/PDF com citações inline)
- Research de múltiplas fases com critique e refinement
- O rigor importa mais que a velocidade

Para ativar o workflow nativo: `/deep-research <pergunta>` ou use a keyword `ultracode` no prompt. Monitore com `/workflows`.

---

## 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-guide.md](./reference/html-generation-guide.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:**
- Instalar dependências antes de rodar qualquer script: `pip install -r requirements.txt` (no diretório da skill)
- `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 `docs/research/[topic]_[YYYYMMDD]/` in the project):**
- 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.