Use when work has genuinely independent streams or distinct builder, evaluator, domain, and integration roles that require bounded multi-agent command scaled from 5 to 100+ agents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a defined problem needs diverse ideas, cross-domain combinations, and cheap experiments instead of a single untested answer.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
<skill_contract> <input>Defined problem, target user, desired change, constraints, prior attempts, and experiment budget.</input> <output>Three non-equivalent options with scored mechanisms and falsifiable minimum experiments.</output> <done>Each selected option has a hypothesis, smallest artifact, participant, threshold, budget, stop rule, and failure learning.</done> <non_goals>Unbounded brainstorming, novelty without utility, polished prose before diversity, or presenting ideas as validated demand.</non_goals>
Create option value by combining mechanisms, constraints, and analogies, then convert the strongest options into minimum experiments.
Provide: problem, target user, desired change, constraints, existing attempts, and experiment budget. Optional: domains or concepts to combine.
Rewrite the request as For [user], change [state] under [constraints], measured by [signal]. Extract reusable building blocks: actors, assets, mechanisms, channels, incentives, and constraints.
<unknowns_gate>
If no user, problem, or constraint is available, return NEEDS_INPUT with one discriminating probe. Treat market demand, technical feasibility, and user behavior as testable unknowns, not assumptions to hide.
</unknowns_gate>
<execute>Do not optimize prose before option diversity. A useful failed experiment is better than an impressive concept with no falsifier.
</execute> <evaluate>Check that the top three differ in mechanism, fit constraints, expose their largest unknown, and can be tested cheaply. Remove ideas that are only features, slogans, or unsupported scale claims. Recombine once if all finalists share the same failure mode.
</evaluate>NEEDS_INPUT: the problem frame lacks a user or constraint.INSUFFICIENT_EVIDENCE: ranking depends on unavailable market or technical facts; mark provisional and probe.VERIFY_FAILED: finalists are duplicates or have no falsifiable test; regenerate around different mechanisms.BUDGET_STOP: no experiment fits the budget; return the cheapest information-gathering action.Return status, result (idea clusters and top-three experiments), evidence (inputs and scoring basis), unknowns, and next_action.
</skill_contract>
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Use when work has genuinely independent streams or distinct builder, evaluator, domain, and integration roles that require bounded multi-agent command scaled from 5 to 100+ agents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when designing or refactoring a model-native engineering workflow with bounded autonomy, probes, custom evaluation, durable state, and verified write-back.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when property-service operations need an AI plus ontology plus DMAIC design for work orders, dispatch, quotes, evidence, CTQ metrics, and control dashboards.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a personal or team operating system needs a bounded redesign using Four-C, closed-loop controls, 70/30 allocation, 3B creativity, experiments, and prediction-error learning.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a goal must be converted into a repeatable behavior, cue, SOP, review cadence, and identity-aligned reinforcement.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when source material must be transformed into a compact, evidence-aware model for learning, decisions, or an Obsidian concept note.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.