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 goal must be converted into a repeatable behavior, cue, SOP, review cadence, and identity-aligned reinforcement.
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>Actor, desired outcome, current behavior, context, constraints, baseline evidence, and review horizon.</input> <output>An agency-preserving HAS behavior system with cues, effort levels, SOP, measures, recovery, and review cadence.</output> <done>The target behavior is observable, runnable on a low-motivation day, evidenced, and assigned one review owner.</done> <non_goals>Coercion, unsupported identity claims, vague motivation advice, or optimizing lagging outcomes without behaviors.</non_goals>
Convert an outcome into the smallest observable behavior system that can survive low-motivation days. Preserve agency; optimize the environment before blaming the actor.
Provide: desired outcome, actor, context, current behavior, constraints, review horizon, and available evidence. Optional: failed attempts and environmental cues.
<unknowns_gate>
Classify unknowns as known, probeable, testable, or blocked. If actor, target behavior, or safety boundary is absent, return NEEDS_INPUT with one minimal question. A reversible assumption is allowed only when labeled and paired with a same-cycle test.
</unknowns_gate>
<execute>Build the HAS sequence:
trigger -> action -> evidence -> recovery -> stop so a missed run has a next move.Reduce friction before adding motivation. Prefer defaults, visible cues, prepared tools, and short feedback latency. Keep the minimum behavior useful rather than ceremonial.
</execute> <evaluate>Check that the behavior is observable, starts in a named context, fits the minimum effort budget, produces evidence, and has a recovery path. Reject plans that depend on constant willpower, hide coercion, or measure only outcomes. If the check fails, revise the highest-friction element once and re-evaluate.
</evaluate><state_contract>
Persist {run_id, status, attempt, budget, evidence, unknowns, last_error, next_action} plus baseline, current H1-H5 design, review date, and revision history. Update atomically after each review; never overwrite prior observations.
</state_contract>
NEEDS_INPUT: a target, actor, or boundary is missing; ask one narrow probe.INSUFFICIENT_EVIDENCE: baseline is unknown; run a short observation period before optimizing.VERIFY_FAILED: the behavior is not observable, feasible, or linked to the outcome; revise one variable.BUDGET_STOP: the review horizon or effort budget is exhausted; preserve state and report the next experiment.Return status, result (H1-H5 plan), evidence (baseline and measures), unknowns, and next_action (owner plus review date).
</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 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.
Use when a long-running agent task needs context budgeting, checkpointing, compaction, retrieval, or capability-based model routing.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.