uniswap Plan Swarm
Refine plans through collaborative multi-agent expert discussion.
⚠️ Cost Warning: This skill uses the Opus model and spawns up to 10 agents for multi-round discussions. A typical plan-swarm session consumes 5-20x more tokens than a standard skill invocation. Consider using
review-planfor simpler validation needs.
When to Activate
- User wants multiple expert perspectives
- Plan needs collaborative refinement
- Complex trade-offs require discussion
- Consensus building needed
- User asks for "expert opinions" or "different perspectives"
Key Features
- Intelligent Agent Selection: up to 10 agents based on plan context
- True Collaboration: Multi-round discussions
- Constructive Disagreement: Respectful challenging of ideas
- Consensus Building: Resolution of conflicts
Quick Process
Phase 1: Context & Agent Selection
- Analyze plan domains (frontend, backend, security, etc.)
- Select the relevant specialized agents, up to 10
- Brief each agent on the plan
Phase 2: Multi-Round Discussion
- Round 1: Initial perspectives from each agent
- Round 2: Cross-domain discussion and response
- Round 3: Consensus building (if needed)
Phase 3: Synthesis
- Compile feedback across rounds
- Document consensus recommendations
- Identify trade-offs and open questions
Agent Selection Guidelines
These are ceilings, not quotas. Staff only the dimensions the plan actually raises - a plan touching one domain gets one specialist, even if it is "complex". Never spawn an agent for analysis you could finish in a handful of direct tool calls.
| Plan Complexity | Agents (max) |
|---|---|
| Simple | up to 2 |
| Medium | up to 5 |
| Complex | up to 10 |
Output Format
Generates refinement document with:
- Executive summary
- Agent participants and focus areas
- Consensus recommendations by category
- Design decisions and trade-offs
- Open questions requiring human decision
- Dissenting opinions with rationale
- Next steps
Discussion Guidelines
Agents are encouraged to:
- Be direct and state opinions clearly
- Challenge constructively with alternatives
- Build on other agents' ideas
- Change positions when persuaded
- Acknowledge expertise limits