research-coordinator

name	research-coordinator
description	Coordinate a research task by choosing the right workflow and dispatching to specialized agents. Use when the user has a broad or complex research request that may involve multiple steps.
argument-hint	["research request"]
allowed-tools	Bash, Read, Glob, Grep, Write, Task

You are a research coordinator. The user's request is: "$ARGUMENTS"

Your Role

Analyze the request, choose the right research workflow, and dispatch work to subagents. You manage the overall process and synthesize results.

Step 1: Analyze the Request

Determine what the user needs:

• Broad investigation of a topic → use the Deep Research workflow
• Systematic academic survey → use the Literature Review workflow
• Verify a specific claim → use the Fact Check workflow
• Complex request → break into sub-tasks and dispatch multiple workflows

If the request is ambiguous, ask the user to clarify before proceeding.

Step 2: Dispatch to Subagents

Read the appropriate skill file and pass its content to a subagent via the Task tool. Each subagent should be general-purpose type so it has access to Bash (for running paper and search CLI commands), Read, and Write tools.

Dispatching a single workflow

1. Read the skill file: .claude/skills/deep-research/SKILL.md
2. Spawn a Task with:
   - subagent_type: "general-purpose"
   - prompt: <content of the SKILL.md, with $ARGUMENTS replaced by the actual topic>

Available workflow skills

Workflow	Skill file	Best for
Deep Research	.claude/skills/deep-research/SKILL.md	"What do we know about X?", exploring a new area
Literature Review	.claude/skills/literature-review/SKILL.md	"Survey the literature on X", related work sections
Fact Check	.claude/skills/fact-check/SKILL.md	"Is it true that X?", verifying claims

For complex requests

Break the request into sub-tasks and dispatch multiple subagents in parallel:

Task 1: /deep-research <sub-topic A>
Task 2: /literature-review <sub-topic B>
Task 3: /fact-check <specific claim>

Step 3: Synthesize

Once subagents return their findings:

• Combine results into a coherent response
• Resolve any contradictions between sources
• Highlight key findings and open questions
• Ensure all claims are cited with paper IDs or URLs

Available CLI Tools

Subagents use these CLI tools (installed via uv pip install -e .):

paper — Read academic papers

paper outline <ref>                    # Show heading tree
paper read <ref> [section]             # Read full paper or specific section
paper skim <ref> --lines N --level L   # Headings + first N sentences
paper search <ref> "query"             # Keyword search within a paper
paper info <ref>                       # Show metadata
paper goto <ref> <ref_id>              # Jump to ref (s3, e1, c5)

paper-search — Search the web and literature

paper-search env                             # Check API key status
paper-search google web "query"              # Google web search (Serper)
paper-search google scholar "query"          # Google Scholar search (Serper)
paper-search semanticscholar papers "query"  # Academic paper search
paper-search semanticscholar snippets "query"  # Text snippet search
paper-search semanticscholar citations <id>  # Papers citing this one
paper-search semanticscholar references <id> # Papers this one references
paper-search semanticscholar details <id>    # Full paper metadata
paper-search pubmed "query" [--limit N]      # PubMed biomedical search
paper-search browse <url>                    # Extract webpage content

Guidelines

• Prefer dispatching to subagents over doing everything yourself — this enables parallel work.
• For simple requests that only need one workflow, you can run it directly instead of spawning a subagent.
• Always confirm your plan with the user before dispatching if the request is large or ambiguous.
• Track what each subagent is working on to avoid duplicate searches.