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Skills/199-biotechnologies/claude-deep-research-skill/deep-research
deep-research logo

deep-research

199-biotechnologies/claude-deep-research-skill
8K installs768 stars
Run it on Hostinger, 20% off →Your friend gets 20% off too, using this linkFree API →|View on GitHub|Create your own skill →

Installation

npx skills add https://github.com/199-biotechnologies/claude-deep-research-skill --skill deep-research

Summary

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.

SKILL.md

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

PhaseNameQuickStdDeepUltra
1SCOPEYYYY
2PLAN-YYY
3RETRIEVEYYYY
4TRIANGULATE-YYY
4.5OUTLINE REFINEMENT-YYY
5SYNTHESIZE-YYY
6CRITIQUE--YY
7REFINE--YY
8PACKAGEYYYY

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.

Score

0–100
69/ 100

Grade

C

Popularity21/30

8,155 installs — solid traction.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Deep Research skill score badge previewScore badge

Markdown

[![Deep Research skill](https://www.claudemarket.ai/skills/199-biotechnologies/claude-deep-research-skill/deep-research/badges/score.svg)](https://www.claudemarket.ai/skills/199-biotechnologies/claude-deep-research-skill/deep-research)

HTML

<a href="https://www.claudemarket.ai/skills/199-biotechnologies/claude-deep-research-skill/deep-research"><img src="https://www.claudemarket.ai/skills/199-biotechnologies/claude-deep-research-skill/deep-research/badges/score.svg" alt="Deep Research skill"/></a>

Deep Research FAQ

How do I install the Deep Research skill?

Run “npx skills add https://github.com/199-biotechnologies/claude-deep-research-skill --skill deep-research” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Deep Research skill do?

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. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Deep Research skill free?

Yes. Deep Research is a free, open-source skill published from 199-biotechnologies/claude-deep-research-skill. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Deep Research work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Deep Research works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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