| 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.
