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 manual work, prior sessions, or an operator interview must be audited for repeatable skill, automation, or bounded-loop candidates.
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>Bounded manual notes, prior-session evidence, or interview answers describing work, repetition, decisions, and constraints.</input> <output>A deterministic audit with mode, evidence-bounded candidates, skill/automation/loop decisions, permissions, and a typed handoff.</output> <done>Every recommendation points to supplied evidence, remains within the input bound, and has an explicit supervised promotion decision.</done> <non_goals>Editing skills, changing schedules, mutating Vault state, inventing metrics, or executing an unapproved external action.</non_goals>
Provide: objective, evidence source, mode (manual, session, or interview), size bound, candidate limit, operator/approval owner, and desired handoff.
<unknowns_gate> Separate observed repetition from a proposed improvement. Missing frequency, owner, verifier, permission, or stop condition makes a candidate review-only; it does not justify a schedule or autonomous loop. </unknowns_gate>
<execute> Extract a bounded list of distinct work candidates. For each candidate, decide:SKILL_CANDIDATE when the work has a reusable procedure or multiple steps; otherwise KEEP_MANUAL.AUTOMATION_CANDIDATE only when repetition or a clear trigger is evidenced; otherwise HOST_REQUIRED_REVIEW.LOOP_CANDIDATE only when retry, monitoring, polling, follow-up, or a repeat-until condition is evidenced; otherwise NO_LOOP.Record the evidence snippet, approval owner, verifier, stop condition, and side-effect expectation. Promotion is a separate supervised action with a receipt and rollback path. </execute>
<evaluate> Reject recommendations that rely on an unbounded input, an inferred external permission, a fluent success claim, or a video/demo as authority. Check candidate count, source hash, evidence linkage, and `side_effect_count: 0`. </evaluate>Return NEEDS_INPUT for missing evidence or a required authority decision, INSUFFICIENT_EVIDENCE when repetition cannot be supported, and VERIFY_FAILED when a recommendation cannot be traced to the supplied source. Preserve the audit receipt and do not promote.
Return status, result, evidence, unknowns, and next_action. The result includes mode, source hash, bounded candidates, skill/automation/loop decisions, approval, verifier, stop condition, and side-effect count.
INSUFFICIENT_EVIDENCE or request a frequency probe.side_effect_count is zero.</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.