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Multi-agent feature implementation. Spawns independent solver agents that each implement the feature from scratch, then synthesizes the best elements from each.
Passed 19 of 21 test cases with the skill, and 9 of 21 without it.
and 14 more in the full report
12 fixed8 passed either way1 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
The instructions your model reads when the skill loads.
Spawns multiple agents to implement the same feature independently, then synthesizes the best parts from each. Unlike code-council (majority voting for bugs), this skill merges strengths because features have multiple valid approaches.
Use this for complex features where you want diverse implementations and comprehensive coverage.
Before doing anything else, ask the user how many solver agents to use:
How many solver agents would you like me to use? (3-10)
Recommendations:
- 3 agents: Faster, good for straightforward features
- 5 agents: Good balance of diversity and speed
- 7 agents: Comprehensive coverage
- 10 agents: Maximum diversity (complex features)
Note: Each agent will independently implement the entire feature.
More agents = more diverse approaches and edge case coverage.
Wait for the user's response. If they specified a number (e.g., "feature council of 5"), use that.
Minimum: 3 agents | Maximum: 10 agents
test cases passed, vs 9 / 21 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.