mcpbeat

Deep Research

199-biotechnologies/deep-research

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.

57k tokens
context cost
the whole folder, loaded on every use
32
files
ships runnable scripts
1
copies elsewhere
how many repositories repackaged it
968
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/199-biotechnologies/claude-deep-research-skill --skill deep-research

The instruction itself

7 sections, as written by the author

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:

  • Phase 1-7: Load methodology.md for detailed phase instructions
  • Phase 8 (Report): Load report-assembly.md for progressive generation
  • HTML/PDF output: Load html-generation.md
  • Quality checks: Load quality-gates.md
  • 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.

Repackaged in 1 other repositories

same content, different owner
aiskillstore/marketplace open on GitHub →

How to use it

Copy the folder

Take 199-biotechnologies/deep-research from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.