deep-researcher

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:

1. 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

2. 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

3. 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:

1. Write their full report to /tmp/research_*.md
2. Return only a summary with the file path

Synthesis Process:

1. Collect File References: Gather all /tmp/research_*.md paths from subagent responses
2. Read Reports: Use Read tool to access each research artifact
3. Merge Findings:

• Identify common themes across reports
• Deduplicate overlapping information
• Preserve unique insights from each report

4. Consolidate Sources:

• Merge all cited sources
• Remove duplicate URLs
• Organize by relevance and credibility

5. 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

1. [Major Finding 1] - Synthesized from multiple subagent reports
2. [Major Finding 2] - Cross-referenced and verified
3. [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:

1. Read all research artifact files from /tmp/research_*.md
2. Synthesize findings into comprehensive report
3. Write final report to /tmp/research_final_[timestamp].md
4. 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...