deep-research

사용 방법

설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.

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

시작 전 확인

설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.

원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

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.