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multi-agent-researcher

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Multi-Agent Research Coordinator

Purpose

Transform complex research questions into comprehensive reports by:

  1. Decomposing broad topics into 2-4 focused subtopics
  2. Spawning specialized researcher agents in parallel
  3. Synthesizing findings into cohesive final report
  4. Saving structured outputs for reference

When to Use

Auto-invoke when user asks:

  • Search/Discovery: "Search what is [topic]", "Find information about [subject]", "Look up [technology]", "Discover [patterns]"
  • Investigation: "Research [topic]", "Investigate [subject]", "Analyze [phenomenon]", "Study [field]", "Explore [domain]"
  • Collection: "Gather information about [subject]", "Collect data on [topic]", "Compile resources for [area]"
  • Learning: "Learn about [subject]", "Tell me about [topic]", "Dig into [technology]", "Delve into [concept]"
  • Contextual: "What are the latest developments in [field]?", "Comprehensive analysis of [topic]", "Deep dive into [subject]", "State of the art in [domain]", "Best practices for [area]"

Do NOT invoke for:

  • Simple factual questions ("What is the capital of France?")
  • Decision evaluation ("Should I use X or Y?")
  • Code-related tasks ("Debug this function", "Write a script")

Orchestration Workflow

Phase 1: Query Analysis & Decomposition

Step 1.1: Understand the Research Question Analyze user's query to identify core topic, scope, and intent.

Step 1.2: Decompose into Subtopics Break topic into 2-4 focused subtopics that are:

  • Mutually exclusive (minimal overlap)
  • Collectively exhaustive (cover whole topic)
  • Independently researchable
  • Together provide comprehensive coverage

Decomposition Patterns:

Temporal: Past → Current → Future Categorical: Category 1, 2, 3 Stakeholder: Technical → Business → Policy → User Problem-Solution: Problem → Solutions → Gaps → Future Geographic: Region A → Region B → Comparison

Step 1.3: Create Research Plan Use TodoWrite to track:

- [ ] Decompose query into subtopics
- [ ] Spawn researcher 1: [subtopic]
- [ ] Spawn researcher 2: [subtopic]
- [ ] Spawn researcher 3: [subtopic]
- [ ] Synthesize findings
- [ ] Save final report

Phase 2: Parallel Research Execution

Step 2.1: Spawn Researcher Agents in Parallel

For each subtopic, create a Task tool call with:

subagent_type: "researcher"
description: "Research {subtopic name}"
prompt: "Research the following subtopic in depth:

**Subtopic**: {Subtopic name}
**Context**: Part of research on '{original topic}'
**Focus**: {Specific guidance}

Conduct thorough web research, gather authoritative sources, extract key findings, and save results to files/research_notes/{subtopic-slug}.md"

Critical: Spawn ALL researchers in parallel (multiple Task calls in same message), not sequentially.

Step 2.2: Monitor Completion Update TodoWrite as researchers complete.

Step 2.3: Verify All Complete Use Glob to confirm all files exist: files/research_notes/*.md


Phase 3: Synthesis & Report Generation

⚠️ CRITICAL: ARCHITECTURAL ENFORCEMENT ACTIVE ⚠️

YOU DO NOT HAVE WRITE TOOL ACCESS when this skill is active. The allowed-tools frontmatter explicitly EXCLUDES the Write tool to enforce proper workflow delegation.

YOU CANNOT:

  • ❌ Write synthesis reports yourself
  • ❌ Create files in files/reports/ directory
  • ❌ Bypass the report-writer agent

YOU MUST:

  • ✅ Spawn report-writer agent via Task tool
  • ✅ Delegate all synthesis and report writing to the agent
  • ✅ Read the completed report and deliver to user

Step 3.1: Verify Research Completion

  1. Use Glob to confirm all research notes exist: files/research_notes/*.md
  2. Verify count matches number of spawned researchers
  3. If any missing: investigate and complete before synthesis

Step 3.2: Spawn Report-Writer Agent (MANDATORY)

This is the ONLY synthesis approach - there is no "Option A" or "Option B". You MUST use the report-writer agent because you lack Write tool permissions.

Task:
subagent_type: "report-writer"
description: "Synthesize research findings into comprehensive report"
prompt: "Synthesize research into comprehensive report:

**Original Question**: {user query}
**Subtopics Researched**: {list all subtopics}
**Notes Location**: files/research_notes/

## Your Tasks:
1. Read ALL research notes from files/research_notes/
2. Identify themes, patterns, and contradictions across notes
3. Synthesize findings into cohesive narrative
4. Cite sources from research notes
5. Add cross-cutting insights beyond individual notes
6. Save comprehensive report to files/reports/{topic-slug}_{timestamp}.md

## Report Structure:
- Executive Summary
- Key Findings (with evidence from research notes)
- Detailed Analysis by subtopic
- Cross-Cutting Themes
- Contradictions and Debates
- Gaps and Limitations
- Source Bibliography

Use the timestamp format: $(date +\"%Y%m%d-%H%M%S\") for the filename."

Step 3.3: Monitor Agent Completion

After spawning report-writer agent, wait for completion. The agent will:

  • Read all research notes
  • Synthesize findings
  • Write comprehensive report to files/reports/
  • Return completion message with file path

Phase 4: Deliver Results

Step 4.1: Create User Summary

# Research Complete: {Topic}

Comprehensive research completed with {N} specialized researchers.

## Key Findings
1. {Finding 1}
2. {Finding 2}
3. {Finding 3}

## Research Scope
{N} subtopics investigated:
- {Subtopic 1}
- {Subtopic 2}
- {Subtopic 3}

## Files Generated
**Research Notes**: `files/research_notes/`
- {file1}.md
- {file2}.md
- {file3}.md

**Final Report**: `files/reports/{filename}.md`

## Next Steps
{Optional suggestions}

Step 4.2: Update TodoWrite Mark all items complete.


Best Practices

Good Decomposition

✅ 2-4 subtopics (sweet spot: 3) ✅ Distinct but related ✅ Comprehensive coverage ✅ Independently researchable

❌ Too many (>5) ❌ Too few (1) ❌ Significant overlap ❌ Too narrow or too broad

Parallel Execution

  • Always spawn researchers simultaneously
  • Never sequential unless dependent
  • Provide context to each researcher
  • Reasonable scope (10-15 min each)

Synthesis Quality

  • Read ALL notes
  • Find connections across subtopics
  • Note contradictions explicitly
  • Cite sources
  • Add insights beyond individual notes

Error Handling

Researcher Fails: Try replacement, proceed with others, note gap No Results Found: Accept partial, note limitation Contradictory Findings: Document all perspectives explicitly Unclear Query: Ask clarifying questions first


Examples

Query: "Research quantum error correction" Decomposition:

  1. Theoretical foundations
  2. Hardware implementations
  3. Commercial viability Researchers: 3 parallel Synthesis: report-writer agent (MANDATORY)

Query: "Investigate cryptocurrency market 2025" Decomposition:

  1. Market metrics & players
  2. Regulatory landscape
  3. Technology evolution
  4. Institutional trends Researchers: 4 parallel Synthesis: report-writer agent (MANDATORY)

Remember: Quality depends on good decomposition, thorough researchers, insightful synthesis, and clear user communication.

Source

git clone https://github.com/zubayer0077/Claude-Multi-Agent-Research-System-Skill/blob/main/.claude/skills/multi-agent-researcher/SKILL.mdView on GitHub

Overview

Multi-Agent Researcher coordinates 2-4 specialized researcher agents to break a broad topic into focused subtopics, run parallel investigations, and synthesize findings into a cohesive final report via a mandated report-writer delegation. This approach delivers comprehensive coverage, faster turnaround, and auditable sources.

How This Skill Works

The skill first decomposes the user query into 2-4 mutually exclusive subtopics and creates a research plan. It then spawns parallel researcher agents via Task prompts to investigate each subtopic, saves their notes to files/research_notes/{subtopic-slug}.md, and monitors completion with TodoWrite and Glob. Finally, it delegates synthesis to a report-writer agent (no direct writing by the agent) and reads back the completed report for delivery to the user.

When to Use It

  • Search/Discovery: 'Search what is [topic]', 'Find information about [subject]', 'Look up [technology]', 'Discover [patterns]'
  • Investigation: 'Research [topic]', 'Investigate [subject]', 'Analyze [phenomenon]', 'Study [field]', 'Explore [domain]'
  • Collection: 'Gather information about [subject]', 'Collect data on [topic]', 'Compile resources for [area]'
  • Learning: 'Learn about [subject]', 'Tell me about [topic]', 'Dig into [technology]', 'Delve into [concept]'
  • Contextual: 'What are the latest developments in [field]?', 'Comprehensive analysis of [topic]', 'Deep dive into [subject]', 'State of the art in [domain]', 'Best practices for [area]'

Quick Start

  1. Step 1: Decompose the query into 2-4 subtopics with clear scope.
  2. Step 2: Spawn parallel researcher agents for each subtopic and prompt with focused guidance.
  3. Step 3: Delegate synthesis to the report-writer agent, read the final report, and deliver to the user.

Best Practices

  • Clearly define 2-4 mutually exclusive subtopics to ensure full coverage with minimal overlap.
  • Spell out subtopic prompts and context to guide researchers toward authoritative sources.
  • Spawn all researcher agents in parallel to maximize speed and reduce bottlenecks.
  • Standardize research notes (files/research_notes/{subtopic-slug}.md) and capture citations.
  • Always delegate synthesis to the report-writer agent and verify the final report before delivery.

Example Use Cases

  • Topic: Global renewable energy integration; subtopics include grid stability, storage tech, policy incentives, and cost trends, with a final synthesized report on feasibility and recommendations.
  • Topic: AI ethics and governance; subtopics cover principles, enforcement mechanisms, case studies, and international coordination, leading to a governance blueprint.
  • Topic: Urban transportation electrification; subtopics explore charging infrastructure, grid impact, economics, and equity considerations, yielding a policy and implementation roadmap.
  • Topic: Quantum computing progress; subtopics address hardware, software stack, applications, and commercialization, resulting in a technology readiness assessment.
  • Topic: Cybersecurity threat landscape; subtopics examine threat actors, defenses, regulatory landscape, and incident response, producing a strategic security plan.

Frequently Asked Questions

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