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/claude-deep-research-skill

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

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claude-deep-research-skill
1.2k1 skill

Claude Code

$ npx -y skills add 199-biotechnologies/claude-deep-research-skill --skill claude-deep-research-skill --agent claude-code

SKILL.md

claude-deep-research-skill.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.

Read more
@199-biotechnologies
Ships withclaude-deep-research-skill

Enterprise-grade research engine for Claude Code. Produces citation-backed reports with source credibility scoring, multi-provider search, and automated validation.

Stars on GitHub

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stars in total
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Star history of 199-biotechnologies/claude-deep-research-skill on GitHub, a chart by star-history.com

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Questions about claude-deep-research-skill.

Which coding agents can use claude-deep-research-skill?
claude-deep-research-skill installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.