multi-agent-researcher — community multi-agent-researcher, Claude-Multi-Agent-Research-System-Skill, community, ide skills, Claude Code, Cursor, Windsurf

v2.1.2
GitHub

About this Skill

Perfect for AI Research Agents needing parallel execution and multi-agent coordination for efficient investigations. 🧩 Orchestrate multi-agent research with parallel execution and audits for efficient, structured investigations and seamless workflow planning.

zubayer0077 zubayer0077
[0]
[0]
Updated: 3/5/2026

Agent Capability Analysis

The multi-agent-researcher skill by zubayer0077 is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance.

Ideal Agent Persona

Perfect for AI Research Agents needing parallel execution and multi-agent coordination for efficient investigations.

Core Value

Empowers agents to decompose complex research questions into focused subtopics, spawn specialized researcher agents in parallel, and synthesize findings into cohesive reports using structured workflow planning and audits, leveraging parallel execution and multi-agent research coordination protocols.

Capabilities Granted for multi-agent-researcher

Decomposing broad research topics into focused subtopics for in-depth analysis
Spawning specialized researcher agents in parallel for efficient information gathering
Synthesizing findings from multiple agents into comprehensive, cohesive final reports

! Prerequisites & Limits

  • Requires multi-agent ecosystem support
  • Dependent on agent specialization and availability for parallel execution
Labs Demo

Browser Sandbox Environment

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Boot Container Sandbox

multi-agent-researcher

Install multi-agent-researcher, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
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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

markdown
1# Research Complete: {Topic} 2 3Comprehensive research completed with {N} specialized researchers. 4 5## Key Findings 61. {Finding 1} 72. {Finding 2} 83. {Finding 3} 9 10## Research Scope 11{N} subtopics investigated: 12- {Subtopic 1} 13- {Subtopic 2} 14- {Subtopic 3} 15 16## Files Generated 17**Research Notes**: `files/research_notes/` 18- {file1}.md 19- {file2}.md 20- {file3}.md 21 22**Final Report**: `files/reports/{filename}.md` 23 24## Next Steps 25{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.

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is multi-agent-researcher?

Perfect for AI Research Agents needing parallel execution and multi-agent coordination for efficient investigations. 🧩 Orchestrate multi-agent research with parallel execution and audits for efficient, structured investigations and seamless workflow planning.

How do I install multi-agent-researcher?

Run the command: npx killer-skills add zubayer0077/Claude-Multi-Agent-Research-System-Skill/multi-agent-researcher. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for multi-agent-researcher?

Key use cases include: Decomposing broad research topics into focused subtopics for in-depth analysis, Spawning specialized researcher agents in parallel for efficient information gathering, Synthesizing findings from multiple agents into comprehensive, cohesive final reports.

Which IDEs are compatible with multi-agent-researcher?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for multi-agent-researcher?

Requires multi-agent ecosystem support. Dependent on agent specialization and availability for parallel execution.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add zubayer0077/Claude-Multi-Agent-Research-System-Skill/multi-agent-researcher. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use multi-agent-researcher immediately in the current project.

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