Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Three AI agents independently implement the same coding task with different optimization priorities (simplicity, speed, robustness), then cross-evaluate each other using weighted numeric scores across five dimensions. The highest-scoring or hybrid solution is delivered alongside a comparison report and decision rationale.
Rather than accepting a single AI-generated solution, you get three independently developed implementations with objective cross-evaluation scores, making trade-offs explicit and selection data-driven.
When to use it
- Choosing between a readable vs performant sorting implementation
- Getting three independent solutions to a concurrency problem before picking one
- Generating a documented comparison for a code review or architecture decision
- Stress-testing edge case coverage across independent implementations
- Deciding whether to optimize for speed or stability on a new API endpoint
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Implement a thread-safe rate limiter
- 1Spawn three agents in parallel: Agent A focuses on simplicity, Agent B on speed, Agent C on robustness
- 2Each agent writes a complete runnable implementation with tests and a SUMMARY.md
- 3Each agent evaluates the other two implementations across simplicity, speed, stability, corner cases, and maintainability
- 4Each agent produces a SCORECARD.md with numeric scores for all three solutions
- 5Select the winner based on score margins and output the final solution, COMPARISON_REPORT.md, and DECISION_RATIONALE.md
A runnable winning implementation with a full comparison report and transparent decision rationale