home / skills / poemswe / co-researcher / research

This skill coordinates intelligent agents to conduct systematic research, generate plans, execute tasks, and synthesize findings into a final report.

npx playbooks add skill poemswe/co-researcher --skill research

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: research
description: Start a research project with intelligent agent orchestration
metadata:
  short-description: Intelligent Research Orchestration
---

# /research - Intelligent Research Orchestration

I'll coordinate specialized agents to conduct systematic research on your topic.

## Research Topic
**Query**: $ARGUMENTS

## 🤖 Orchestration Mode

I'll analyze your query and build an execution plan using specialized agents.

**Agent Registry**:
- `literature-reviewer`: Academic source search, citation chains
- `critical-analyzer`: Fallacy detection, bias identification
- `hypothesis-explorer`: Hypothesis formulation, variable mapping
- `lateral-thinker`: Cross-domain analogies, creative thinking
- `qual-researcher`: Thematic analysis, coding strategies
- `quant-analyst`: Statistical methods, effect sizes
- `peer-reviewer`: Manuscript evaluation
- `ethics-expert`: IRB compliance, privacy risks
- `grant-writer`: Grant proposal development, funding strategy

**Orchestration Process**:
1. **Classify Query**: Determine research type
2. **Select Agents**: Choose optimal agent sequence
3. **Generate Plan**: Build execution DAG
4. **Present Plan**: Show workflow to user
5. **Execute**: Run agents sequentially, save outputs
6. **Synthesize**: Integrate outputs, save final report

I'm analyzing your research topic to determine the optimal workflow...

**Proposed Plan**:
[I will generate this based on your query]

**Proceed?** (yes/no/modify)

---

## File Writing Protocol

**CRITICAL**: After plan approval, you MUST save all research outputs to files.

### Step 1: Create Output Directory
After user approves the plan, immediately:
1. Use Bash to create timestamped directory: `mkdir -p research-outputs/$(date +%Y-%m-%d_%H-%M-%S)`
2. Store the full path in a variable for subsequent file writes
3. Confirm directory creation to user

### Step 2: Write Research Plan
Use Write tool to create `00-research-plan.md` in the output directory:
```markdown
# Research Plan: [Query]

**Created**: [Timestamp]
**Query**: [User's research query]

## Selected Agents
1. [agent-name] - [purpose]
2. [agent-name] - [purpose]
...

## Execution Plan
[Full plan as presented to user]
```

### Step 3: Write Agent Outputs
After EACH agent completes execution:
1. Use Write tool to save output to `{NN}-{agent-name}.md`
   - NN = sequential number (01, 02, 03, etc.)
   - Example: `01-literature-reviewer.md`, `02-critical-analyzer.md`
2. Format:
```markdown
# {Agent Name} Output

**Agent**: {agent-name}
**Executed**: [Timestamp]

---

[Full agent output - preserve all markdown formatting]
```

### Step 4: Write Final Synthesis
After synthesis completes:
1. Use Write tool to save to `final-synthesis.md`
2. Include complete synthesis with all sections
3. Add metadata header with timestamp and agents used

### Error Handling
- If directory creation fails: warn user and continue with conversation-only output
- If Write fails: log error, notify user, continue execution
- Partial results are acceptable if execution is interrupted

---

**Modes Supported**:
- Default: Interactive
- `--auto`: Automatic execution
- `--plan-only`: Show plan only
- `--manual`: Traditional guided mode

**Templates**: `--template=quick|rigorous|comprehensive`

Overview

This skill coordinates specialized agents to plan and run systematic research projects. It builds a tailored execution plan, runs agents in sequence, and synthesizes their outputs into a consolidated report. It supports interactive, automated, and plan-only modes to fit different workflows.

How this skill works

The skill classifies your research query, selects an optimal sequence of domain agents (e.g., literature-reviewer, quant-analyst, ethics-expert), and generates an execution DAG. After you approve the plan, it executes agents stepwise, saves each agent's output as files in a timestamped output directory, and produces a final synthesis integrating findings and metadata.

When to use it

  • Starting a formal literature review with traceable outputs and citations.
  • Designing or validating hypotheses that require cross-domain input.
  • Preparing a grant proposal or structured manuscript with peer-review style checks.
  • Running mixed-methods projects that need both qualitative coding and quantitative analysis.
  • Assessing ethical, privacy, or compliance risks for planned studies.

Best practices

  • Provide a clear, focused research question and any constraints (timeline, datasets, templates).
  • Choose an execution template (quick, rigorous, comprehensive) to match effort and depth.
  • Review and approve the proposed plan before execution to control agent sequencing.
  • Keep iterations short: run agents, review outputs, then request targeted follow-ups.
  • Preserve outputs by accepting automatic file writes so results remain reproducible.

Example use cases

  • Academic: run a literature-reviewer, critical-analyzer, and quant-analyst to produce a methods-ready report for a systematic review.
  • Product research: use lateral-thinker and qual-researcher to generate cross-domain insights and user-theme coding for feature prioritization.
  • Grant prep: orchestrate grant-writer, ethics-expert, and peer-reviewer to draft and vet a funding proposal.
  • Pilot study planning: combine hypothesis-explorer, quant-analyst, and ethics-expert to produce sample size estimates and IRB considerations.

FAQ

Can I see the plan before any agents run?

Yes — the skill always presents a proposed execution plan and awaits your approval or modification before execution.

How are outputs saved?

After plan approval the skill creates a timestamped output directory and writes a research plan file, each agent's output sequentially, and a final synthesis file with metadata.

What if file creation fails?

The system will warn you, continue with conversation-only outputs, and log write errors so partial results are still usable.