| name | deep-researcher |
| description | Parallel multi-agent research with citations. Use when conducting deep research, competitive analysis, or investigating complex topics. |
Research Command
Conduct deep, parallel research on any topic using multiple specialized subagents.
Research Query
$ARGUMENTS
Research Process
Phase 1: Query Classification (CRITICAL FIRST STEP)
PRIMARY DECISION: Classify the query type to determine research strategy
Query Types:
- BREADTH-FIRST QUERIES (Wide exploration)
- Characteristics: Multiple independent aspects, survey questions, comparisons
- Examples: "Compare all major cloud providers", "List board members of S&P 500 tech companies"
- Strategy: 5-10 parallel subagents, each exploring different aspects
- Each subagent gets narrow, specific tasks
- DEPTH-FIRST QUERIES (Deep investigation)
- Characteristics: Single topic requiring thorough understanding, technical deep-dives
- Examples: "How does transformer architecture work?", "Explain quantum entanglement"
- Strategy: 2-4 subagents with overlapping but complementary angles
- Each subagent explores the same topic from different perspectives
- SIMPLE FACTUAL QUERIES (Quick lookup)
- Characteristics: Single fact, recent event, specific data point
- Examples: "When was GPT-4 released?", "Current CEO of Microsoft"
- Strategy: 1-2 subagents for verification
- Focus on authoritative sources
After Classification, Determine:
- Resource Allocation: Based on query type (1-10 subagents)
- Search Domains: Academic, technical, news, or general web
- Depth vs Coverage: How deep vs how wide to search
Phase 2: Parallel Research Execution
Based on the query classification, spawn appropriate research subagents IN A SINGLE MESSAGE for true parallelization.
CRITICAL: Parallel Execution Pattern
Use multiple Task tool invocations in ONE message, ALL with subagent_type="research-expert".
MANDATORY: Start Each Task Prompt with Mode Indicator
You MUST begin each task prompt with one of these trigger phrases to control subagent behavior:
- Quick Verification (3-5 searches): Start with "Quick check:", "Verify:", or "Confirm:"
- Focused Investigation (5-10 searches): Start with "Investigate:", "Explore:", or "Find details about:"
- Deep Research (10-15 searches): Start with "Deep dive:", "Comprehensive:", "Thorough research:", or "Exhaustive:"
Example Task invocations:
Task(description="Academic research", prompt="Deep dive: Find all academic papers on transformer architectures from 2017-2024", subagent_type="research-expert")
Task(description="Quick fact check", prompt="Quick check: Verify the release date of GPT-4", subagent_type="research-expert")
Task(description="Company research", prompt="Investigate: OpenAI's current product offerings and pricing", subagent_type="research-expert")
This ensures all subagents work simultaneously AND understand the expected search depth through these trigger words.
Filesystem Artifact Pattern:
Each subagent saves full report to /tmp/research_[timestamp]_[topic].md and returns only:
- File path to the full report
- Brief 2-3 sentence summary
- Key topics covered
- Number of sources found
Phase 3: Synthesis from Filesystem Artifacts
CRITICAL: Subagents Return File References, Not Full Reports
Each subagent will:
- Write their full report to
/tmp/research_*.md
- Return only a summary with the file path
Synthesis Process:
- Collect File References: Gather all
/tmp/research_*.md paths from subagent responses
- Read Reports: Use Read tool to access each research artifact
- Merge Findings:
- Identify common themes across reports
- Deduplicate overlapping information
- Preserve unique insights from each report
- Consolidate Sources:
- Merge all cited sources
- Remove duplicate URLs
- Organize by relevance and credibility
- Write Final Report: Save synthesized report to
/tmp/research_final_[timestamp].md
Phase 4: Final Report Structure
The synthesized report (written to file) must include:
Research Report: [Query Topic]
Executive Summary
[3-5 paragraph overview synthesizing all findings]
Key Findings
- [Major Finding 1] - Synthesized from multiple subagent reports
- [Major Finding 2] - Cross-referenced and verified
- [Major Finding 3] - With supporting evidence from multiple sources
Detailed Analysis
[Theme 1 - Merged from Multiple Reports]
[Comprehensive synthesis integrating all relevant subagent findings]
[Theme 2 - Merged from Multiple Reports]
[Comprehensive synthesis integrating all relevant subagent findings]
Sources & References
[Consolidated list of all sources from all subagents, organized by type]
Research Methodology
- Query Classification: [Breadth/Depth/Simple]
- Subagents Deployed: [Number and focus areas]
- Total Sources Analyzed: [Combined count]
- Research Artifacts: [List of all /tmp/research_*.md files]
Research Principles
Quality Heuristics
- Start with broad searches, then narrow based on findings
- Prefer authoritative sources (academic papers, official docs, primary sources)
- Cross-reference claims across multiple sources
- Identify gaps and contradictions in available information
Effort Scaling by Query Type
- Simple Factual: 1-2 subagents, 3-5 searches each (verification focus)
- Depth-First: 2-4 subagents, 10-15 searches each (deep understanding)
- Breadth-First: 5-10 subagents, 5-10 searches each (wide coverage)
- Maximum Complexity: 10 subagents (Claude Code limit)
Parallelization Strategy
- Spawn all initial subagents simultaneously for speed
- Each subagent performs multiple parallel searches
- 90% time reduction compared to sequential searching
- Independent exploration prevents bias and groupthink
Execution
Step 1: CLASSIFY THE QUERY (Breadth-first, Depth-first, or Simple factual)
Step 2: LAUNCH APPROPRIATE SUBAGENT CONFIGURATION
Example Execution Patterns:
BREADTH-FIRST Example: "Compare AI capabilities of Google, OpenAI, and Anthropic"
- Classification: Breadth-first (multiple independent comparisons)
- Launch 6 subagents in ONE message with focused investigation mode:
- Task 1: "Investigate: Google's current AI products, models, and capabilities"
- Task 2: "Investigate: OpenAI's current AI products, models, and capabilities"
- Task 3: "Investigate: Anthropic's current AI products, models, and capabilities"
- Task 4: "Explore: Performance benchmarks comparing models from all three companies"
- Task 5: "Investigate: Business models, pricing, and market positioning for each"
- Task 6: "Quick check: Latest announcements and news from each company (2024)"
DEPTH-FIRST Example: "How do transformer models achieve attention?"
- Classification: Depth-first (single topic, deep understanding)
- Launch 3 subagents in ONE message with deep research mode:
- Task 1: "Deep dive: Mathematical foundations and formulas behind attention mechanisms"
- Task 2: "Comprehensive: Visual diagrams and step-by-step walkthrough of self-attention"
- Task 3: "Thorough research: Seminal papers including 'Attention is All You Need' and subsequent improvements"
SIMPLE FACTUAL Example: "When was Claude 3 released?"
- Classification: Simple factual query
- Launch 1 subagent with verification mode:
- Task 1: "Quick check: Verify the official release date of Claude 3 from Anthropic"
Each subagent works independently, writes findings to /tmp/research_*.md, and returns a lightweight summary.
Step 3: SYNTHESIZE AND DELIVER
After all subagents complete:
- Read all research artifact files from
/tmp/research_*.md
- Synthesize findings into comprehensive report
- Write final report to
/tmp/research_final_[timestamp].md
- Provide user with:
- Executive summary (displayed directly)
- Path to full report file
- Key insights and recommendations
Benefits of Filesystem Artifacts:
- 90% reduction in token usage (passing paths vs full reports)
- No information loss during synthesis
- Preserves formatting and structure
- Enables selective reading of sections
- Allows user to access individual subagent reports if needed
Now executing query classification and multi-agent research...
