免费在 GitHub 公开

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.

中文简介正在准备中。当前显示原文说明。

aiskillstore/marketplace

查看安装方法

包含的文件（5）

SKILL.md4.2 KB
.gitignore264 B
README.md4.8 KB
requirements.txt579 B
skill-report.json23.6 KB

SKILL.md（原文）

安装前可以查看交给 AI 代理的指令内容。

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.

评价

还没有评价。欢迎分享你的使用感受。

无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

同一仓库的技能

academy-guide

免费

Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

中文简介正在准备中。当前显示原文说明。