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.
中文简介正在准备中。当前显示原文说明。
Use when manual work, prior sessions, or an operator interview must be audited for repeatable skill, automation, or bounded-loop candidates.
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
<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.
中文简介正在准备中。当前显示原文说明。
Use when designing or refactoring a model-native engineering workflow with bounded autonomy, probes, custom evaluation, durable state, and verified write-back.
中文简介正在准备中。当前显示原文说明。
Use when property-service operations need an AI plus ontology plus DMAIC design for work orders, dispatch, quotes, evidence, CTQ metrics, and control dashboards.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
Use when a goal must be converted into a repeatable behavior, cue, SOP, review cadence, and identity-aligned reinforcement.
中文简介正在准备中。当前显示原文说明。
Use when source material must be transformed into a compact, evidence-aware model for learning, decisions, or an Obsidian concept note.
中文简介正在准备中。当前显示原文说明。