数据分析与咨询
#research
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.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
获取
https://deepseekmodel.com/api/download.php?id=199-biotechnologies-claude-deep-research-skill-skill-md&format=skill
下载 .skill
标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
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: 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.
Agent 识别该技能的关键词,点击任意一个即可复制。
该技能未提供触发词。
下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识(skill/v1) |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类(数组) |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接(本页地址) |
| exported_at | 导出时间(每次下载生成) |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数:provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明(Coze / Dify / Claude / 自定义框架) |