claude-deep-research-skill
💡 摘要
通过多阶段方法进行全面的、基于引用的研究,以进行深入分析。
🎯 适合人群
🤖 AI 吐槽: “看起来很能打,但别让配置把人劝退。”
风险:Medium。建议检查:是否执行 shell/命令行指令;是否发起外网请求(SSRF/数据外发);API Key/Token 的获取、存储与泄露风险;文件读写范围与路径穿越风险。以最小权限运行,并在生产环境启用前审计代码与依赖。
name: deep-research description: Conduct enterprise-grade research with multi-source synthesis, citation tracking, and verification. Use when user needs comprehensive analysis requiring 10+ sources, verified claims, or comparison of approaches. Triggers include "deep research", "comprehensive analysis", "research report", "compare X vs Y", or "analyze trends". Do NOT use for simple lookups, debugging, or questions answerable with 1-2 searches.
Deep Research
Core System Instructions
Purpose: Deliver citation-backed, verified research reports through 8-phase pipeline (Scope → Plan → Retrieve → Triangulate → Synthesize → Critique → Refine → Package) with source credibility scoring and progressive context management.
Context Strategy: This skill uses 2025 context engineering best practices:
- Static instructions cached (this section)
- Progressive disclosure (load references only when needed)
- Avoid "loss in the middle" (critical info at start/end, not buried)
- Explicit section markers for context navigation
Decision Tree (Execute First)
Request Analysis
├─ Simple lookup? → STOP: Use WebSearch, not this skill
├─ Debugging? → STOP: Use standard tools, not this skill
└─ 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)
Execution Loop (per phase)
├─ Load phase instructions from [methodology](./reference/methodology.md#phase-N)
├─ Execute phase tasks
├─ Spawn parallel agents if applicable
└─ Update progress
Validation Gate
├─ Run `python scripts/validate_report.py --report [path]`
├─ Pass? → Deliver
└─ Fail? → Fix (max 2 attempts) → Still fails? → Escalate
Workflow (Clarify → Plan → Act → Verify → Report)
AUTONOMY PRINCIPLE: This skill operates independently. Infer assumptions from query context. Only stop for critical errors or incomprehensible queries.
1. Clarify (Rarely Needed - Prefer Autonomy)
DEFAULT: Proceed autonomously. Derive assumptions from query signals.
ONLY ask if CRITICALLY ambiguous:
- Query is incomprehensible (e.g., "research the thing")
- Contradictory requirements (e.g., "quick 50-source ultradeep analysis")
When in doubt: PROCEED with standard mode. User will redirect if incorrect.
Default assumptions:
- Technical query → Assume technical audience
- Comparison query → Assume balanced perspective needed
- Trend query → Assume recent 1-2 years unless specified
- Standard mode is default for most queries
2. Plan
Mode selection criteria:
- Quick (2-5 min): Exploration, broad overview, time-sensitive
- Standard (5-10 min): Most use cases, balanced depth/speed [DEFAULT]
- Deep (10-20 min): Important decisions, need thorough verification
- UltraDeep (20-45 min): Critical analysis, maximum rigor
Announce plan and execute:
- Briefly state: selected mode, estimated time, number of sources
- Example: "Starting standard mode research (5-10 min, 15-30 sources)"
- Proceed without waiting for approval
3. Act (Phase Execution)
All modes execute:
- Phase 1: SCOPE - Define boundaries (method)
- Phase 3: RETRIEVE - Parallel search execution (5-10 concurrent searches + agents) (method)
- Phase 8: PACKAGE - Generate report using template
Standard/Deep/UltraDeep execute:
- Phase 2: PLAN - Strategy formulation
- Phase 4: TRIANGULATE - Verify 3+ sources per claim
- Phase 4.5: OUTLINE REFINEMENT - Adapt structure based on evidence (WebWeaver 2025) (method)
- Phase 5: SYNTHESIZE - Generate novel insights
Deep/UltraDeep execute:
- Phase 6: CRITIQUE - Red-team analysis
- Phase 7: REFINE - Address gaps
Critical: Avoid "Loss in the Middle"
- Place key findings at START and END of sections, not buried
- Use explicit headers and markers
- Structure: Summary → Details → Conclusion (not Details sandwiched)
Progressive Context Loading:
- Load methodology sections on-demand
- Load template only for Phase 8
- Do not inline everything - reference external files
Anti-Hallucination Protocol (CRITICAL):
- Source grounding: Every factual claim MUST cite a specific source immediately [N]
- Clear boundaries: Distinguish between FACTS (from sources) and SYNTHESIS (your analysis)
- Explicit markers: Use "According to [1]..." or "[1] reports..." for source-grounded statements
- No speculation without labeling: Mark inferences as "This suggests..." not "Research shows..."
- Verify before citing: If unsure whether source actually says X, do NOT fabricate citation
- When uncertain: Say "No sources found for X" rather than inventing references
Parallel Execution Requirements (CRITICAL for Speed):
Phase 3 RETRIEVE - Mandatory Parallel Search:
- Decompose query into 5-10 independent search angles before ANY searches
- Launch ALL searches in single message with multiple tool calls (NOT sequential)
- Quality threshold monitoring for FFS pattern:
- Track source count and avg credibility score
- Proceed when threshold reached (mode-specific, see methodology)
- Continue background searches for additional depth
- Spawn 3-5 parallel agents using Task tool for deep-dive investigations
Example correct execution:
[Single message with 8+ parallel tool calls]
WebSearch #1: Core topic semantic
WebSearch #2: Technical keywords
WebSearch #3: Recent 2024-2025 filtered
WebSearch #4: Academic domains
WebSearch #5: Critical analysis
WebSearch #6: Industry trends
Task agent #1: Academic paper analysis
Task agent #2: Technical documentation deep dive
❌ WRONG (sequential execution):
WebSearch #1 → wait for results → WebSearch #2 → wait → WebSearch #3...
✅ RIGHT (parallel execution):
All searches + agents launched simultaneously in one message
4. Verify (Always Execute)
Step 1: Citation Verification (Catches Fabricated Sources)
python scripts/verify_citations.py --report [path]
Checks:
- DOI resolution (verifies citation actually exists)
- Title/year matching (detects mismatched metadata)
- Flags suspicious entries (2024+ without DOI, no URL, failed verification)
If suspicious citations found:
- Review flagged entries manually
- Remove or replace fabricated sources
- Re-run until clean
Step 2: Structure & Quality Validation
python scripts/validate_report.py --report [path]
8 automated checks:
- Executive summary length (50-250 words)
- Required sections present (+ recommended: Claims table, Counterevidence)
- Citations formatted [1], [2], [3]
- Bibliography matches citations
- No placeholder text (TBD, TODO)
- Word count reasonable (500-10000)
- Minimum 10 sources
- No broken internal links
If fails:
- Attempt 1: Auto-fix formatting/links
- Attempt 2: Manual review + correction
- After 2 failures: STOP → Report issues → Ask user
5. Report
CRITICAL: Generate COMPREHENSIVE, DETAILED markdown reports
File Organization (CRITICAL - Clean Accessibility):
1. Create Organized Folder in Documents:
- ALWAYS create dedicated folder:
~/Documents/[TopicName]_Research_[YYYYMMDD]/ - Extract clean topic name from research question (remove special chars, use underscores/CamelCase)
- Examples:
- "psilocybin research 2025" →
~/Documents/Psilocybin_Research_20251104/ - "compare React vs Vue" →
~/Documents/React_vs_Vue_Research_20251104/ - "AI safety trends" →
~/Documents/AI_Safety_Trends_Research_20251104/
- "psilocybin research 2025" →
- If folder exists, use it; if not, create it
- This ensures clean organization and easy accessibility
2. Save All Formats to Same Folder:
Markdown (Primary Source):
- Save to:
[Documents folder]/research_report_[YYYYMMDD]_[topic_slug].md - Also save copy to:
~/.claude/research_output/(internal tracking) - Full detailed report with all findings
HTML (McKinsey Style - ALWAYS GENERATE):
- Save to:
[Documents folder]/research_report_[YYYYMMDD]_[topic_slug].html - Use McKinsey template: mckinsey_template
- Design principles: Sharp corners (NO border-radius), muted corporate colors (navy #003d5c, gray #f8f9fa), ultra-compact layout, info-first structure
- Place critical metrics dashboard at top (extract 3-4 key quantitative findings)
- Use data tables for dense information presentation
- 14px base font, compact spacing, no decorative gradients or colors
- Attribution Gradients (2025): Wrap each citation [N] in
<span class="citation">with nested tooltip div showing source details - OPEN in browser automatically after generation
PDF (Professional Print - ALWAYS GENERATE):
- Save to:
[Documents folder]/research_report_[YYYYMMDD]_[topic_slug].pdf - Use generating-pdf skill (via Task tool with general-purpose agent)
- Professional formatting with headers, page numbers
- OPEN in default PDF viewer after generation
3. File Naming Convention: All files use same base name for easy matching:
research_report_20251104_psilocybin_2025.mdresearch_report_20251104_psilocybin_2025.htmlresearch_report_20251104_psilocybin_2025.pdf
Length Requirements (UNLIMITED with Progressive Assembly):
- Quick mode: 2,000+ words (baseline quality threshold)
- Standard mode: 4,000+ words (comprehensive
优点
- 提供详细的、基于引用的报告。
- 采用结构化的多阶段方法。
- 有效满足复杂的研究需求。
缺点
- 不适合简单查询。
- 深度分析可能需要大量时间。
- 新用户的复杂设置。
相关技能
免责声明:本内容来源于 GitHub 开源项目,仅供展示和评分分析使用。
版权归原作者所有 199-biotechnologies.
