Prompt
Check a dataset before analysis
Find missing values, inconsistent formats, duplicates, and questions that could invalidate results.
ClaudeChatGPTGeminiData & Analytics
Dave AI
Deep Research
Orchestrate multi-phase deep research with web search, memory retrieval, pattern matching, and synthesis into structured findings
instructions.md
--- name: deep-research-6e59f9c21c47 description: Orchestrate multi-phase deep research with web search, memory retrieval, pattern matching, and synthesis into structured findings argument-hint: "<topic>" allowed-tools: mcp__claude-flow__memory_store mcp__claude-flow__memory_search mcp__claude-flow__memory_search_unified mcp__claude-flow__agentdb_hierarchical-store mcp__claude-flow__agentdb_hierarchical-recall mcp__claude-flow__agentdb_pattern-search mcp__claude-flow__agentdb_pattern-store mcp__claude-flow__neural_predict mcp__claude-flow__hooks_intelligence_pattern-search mcp__claude-flow__hooks_intelligence_pattern-store mcp__claude-flow__task_create mcp__claude-flow__task_list mcp__claude-flow__task_summary Bash WebSearch WebFetch Read Write --- # Deep Research Orchestrate multi-phase deep research campaigns that gather, cross-reference, and synthesize information from multiple sources. ## When to use When you need to investigate a complex topic thoroughly — spanning web sources, codebase patterns, stored memory, and external documentation — and produce a structured synthesis. ## Steps 1. **Define research scope** — break the question into 3-7 sub-questions that together answer the main question 2. **Search existing knowledge** — call `mcp__claude-flow__memory_search_unified` and `mcp__claude-flow__agentdb_pattern-search` to check what's already known 3. **Web research** — use `WebSearch` and `WebFetch` to gather external information for each sub-question 4. **Codebase analysis** — use `Bash` (grep/find), `Read` to examine relevant source files 5. **Cross-reference** — compare findings across sources, identify agreements and contradictions 6. **Store findings** — call `mcp__claude-flow__memory_store` with namespace `research` for each key finding 7. **Store patterns** — call `mcp__claude-flow__agentdb_pattern-store` for reusable patterns discovered 8. **Synthesize** — produce a structured research report with: - Executive summary (2-3 sentences) - Key findings (bulleted) - Evidence quality assessment (high/medium/low per finding) - Open questions remaining - Recommended next steps ## Research depth levels - **Quick** — memory search + 1-2 web queries, 2-3 minutes - **Standard** — memory + web + codebase scan, 5-10 minutes - **Deep** — all sources + cross-referencing + pattern storage, 15-30 minutes - **Exhaustive** — deep + spawn sub-agents for parallel research threads, 30+ minutes ## Memory namespaces - `research` — raw findings keyed by topic - `research-synthesis` — completed synthesis reports - `research-sources` — source URLs and references--- name: deep-research-6e59f9c21c47 description: Orchestrate multi-phase deep research with web search, memory retrieval, pattern matching, and synthesis into structured findings argument-hint: "<topic>" allowed-tools: mcp__claude-flow__memory_store mcp__claude-flow__memory_search mcp__claude-flow__memory_search_unified mcp__claude-flow__agentdb_hierarchical-store mcp__claude-flow__agentdb_hierarchical-recall mcp__claude-flow__agentdb_pattern-search mcp__claude-flow__agentdb_pattern-store mcp__claude-flow__neural_predict mcp__claude-flow__hooks_intelligence_pattern-search mcp__claude-flow__hooks_intelligence_pattern-store mcp__claude-flow__task_create mcp__claude-flow__task_list mcp__claude-flow__task_summary Bash WebSearch WebFetch Read Write --- # Deep Research Orchestrate multi-phase deep research campaigns that gather, cross-reference, and synthesize information from multiple sources. ## When to use When you need to investigate a complex topic thoroughly — spanning web sources, codebase patterns, stored memory, and external documentation — and produce a structured synthesis. ## Steps 1. **Define research scope** — break the question into 3-7 sub-questions that together answer the main question 2. **Search existing knowledge** — call `mcp__claude-flow__memory_search_unified` and `mcp__claude-flow__agentdb_pattern-search` to check what's already known 3. **Web research** — use `WebSearch` and `WebFetch` to gather external information for each sub-question 4. **Codebase analysis** — use `Bash` (grep/find), `Read` to examine relevant source files 5. **Cross-reference** — compare findings across sources, identify agreements and contradictions 6. **Store findings** — call `mcp__claude-flow__memory_store` with namespace `research` for each key finding 7. **Store patterns** — call `mcp__claude-flow__agentdb_pattern-store` for reusable patterns discovered 8. **Synthesize** — produce a structured research report with: - Executive summary (2-3 sentences) - Key findings (bulleted) - Evidence quality assessment (high/medium/low per finding) - Open questions remaining - Recommended next steps ## Research depth levels - **Quick** — memory search + 1-2 web queries, 2-3 minutes - **Standard** — memory + web + codebase scan, 5-10 minutes - **Deep** — all sources + cross-referencing + pattern storage, 15-30 minutes - **Exhaustive** — deep + spawn sub-agents for parallel research threads, 30+ minutes ## Memory namespaces - `research` — raw findings keyed by topic - `research-synthesis` — completed synthesis reports - `research-sources` — source URLs and references
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.