Multi-Agent Research Coordinator

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Decompose complex research questions into 2-4 subtopics and coordinate specialized researcher agents in parallel to synthesize detailed comprehensive reports.

Sby Skills Guide Bot
Data & AIIntermediate
3/4/2026
Claude Code
#multi-agent#research#orchestration#parallel-tasks#report-generation

Recommended for

Automated summary

This skill breaks a broad research question into 2-4 subtopics, spawns specialized researcher agents in parallel through the Task tool, then delegates synthesis to a report-writer agent to produce a structured report.

Strengths

  • Explicit decomposition into mutually exclusive, collectively exhaustive subtopics, with several patterns (temporal, categorical, stakeholder, problem-solution, geographic)
  • Genuinely parallel researcher execution with progress tracking via TodoWrite and Glob verification
  • Enforced architecture: the coordinator has no Write access, which forces delegation of report writing instead of bypassing the workflow
  • Persists research notes and the final report to structured files for later reference

Limitations

  • Assumes the 'researcher' and 'report-writer' subagents already exist in the environment; the skill does not define them
  • Relies on subagents being able to do web research, which is outside the coordinator's listed toolset
  • Not suited to simple factual questions or option comparisons, which the skill explicitly excludes
When to use it

Use it when a broad question needs deep source-gathering across multiple angles plus a synthesized written report.

When not to use it

Avoid it for a single factual lookup, a 'should I use X or Y' decision, or a coding task.

Security analysis

Safe
Quality score82/100

The skill only declares Task, Read, Glob, and TodoWrite — no Bash, Write, or network tools. It orchestrates other agents to research and produce a report, with no destructive commands (no rm -rf, curl|sh, base64 payloads), no secret/token exfiltration, and no attempt to disable safety controls. The only minor items are delegated web research and a benign shell date substitution in a prompt. Overall low execution risk.

Findings
  • •Delegates work to subagents ('researcher', 'report-writer') which are expected to perform web research and write files; network activity is delegated rather than explicit
  • •Contains a `$(date +"%Y%m%d-%H%M%S")` shell substitution embedded in an agent prompt — harmless here but relies on shell evaluation by the subagent
  • •Enforces workflow by omitting the Write tool from allowed-tools; the 'architectural enforcement' claim depends on the host honoring the frontmatter
How this analysis is made

Examples

State of the art report
Research the state of the art in WebAssembly runtimes for edge computing and produce a comprehensive report.
Landscape deep dive
Deep dive into the open-source vector database landscape and tell me about the latest developments.
Standards investigation
Investigate the current status of post-quantum cryptography standardization efforts and gather authoritative sources.

name: multi-agent-researcher description: Conduct comprehensive research on any topic by coordinating 2-4 specialized researcher agents in parallel, then synthesizing findings into a detailed report via mandatory report-writer agent delegation allowed-tools: Task, Read, Glob, TodoWrite version: 2.1.2

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.

From the file to a first result

The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.

View the source and supporting files

This download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.

1. Download the file

Download SKILL.md

From the root of a test project, save the file as .skill-downloads/multi-agent-research-coordinator-mmbizjoe/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.

Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.

mkdir -p '.skill-downloads/multi-agent-research-coordinator-mmbizjoe' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/multi-agent-research-coordinator-mmbizjoe/download' --output '.skill-downloads/multi-agent-research-coordinator-mmbizjoe/SKILL.md.part' && mv '.skill-downloads/multi-agent-research-coordinator-mmbizjoe/SKILL.md.part' '.skill-downloads/multi-agent-research-coordinator-mmbizjoe/SKILL.md'

2. Check prerequisites

Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.

Tools declared by the author (check availability in your agent): Task, Read, Glob, TodoWrite

No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.

The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.

3. Load the skill

After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.

Claude Code
test -f '.skill-downloads/multi-agent-research-coordinator-mmbizjoe/SKILL.md' && test -s '.skill-downloads/multi-agent-research-coordinator-mmbizjoe/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/multi-agent-researcher' && cp -R '.skill-downloads/multi-agent-research-coordinator-mmbizjoe' '.claude/skills/multi-agent-researcher'

OpenSkills

OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.

Official OpenSkills installation
openskills install './.skill-downloads/multi-agent-research-coordinator-mmbizjoe'

openskills read 'multi-agent-research-coordinator-mmbizjoe'

Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.

For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.

openskills sync

Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.

4. Try a first task

Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:

Read .skill-downloads/multi-agent-research-coordinator-mmbizjoe/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.

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