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 long-running agent task needs context budgeting, checkpointing, compaction, retrieval, or capability-based model routing.
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>An active objective, runtime context limits, decision-critical state, evidence, budgets, and durable storage.</input> <output>A bounded context manifest, checkpoint, compaction decision, and capability-routing receipt.</output> <done>The next decision can be resumed without losing constraints, permissions, provenance, or failure history.</done> <non_goals>Task orchestration, vendor-specific routing, hidden truncation, or retaining history that cannot change a decision.</non_goals>
Treat context as RAM, durable storage as disk, and tools as retrieval. Pay only for information that changes the next decision or prevents an execution error.
Provide: objective, phase, runtime context limit, token/cost budget, active constraints, current evidence, durable state location, and expected next decisions.
Measure available budget from runtime data; do not infer limits or prices from model names. Partition context into objective, constraints, active state, evidence, open unknowns, and history. Identify the next irreversible or high-cost decision.
</intake><unknowns_gate>
If runtime limits or durable state are unavailable, choose a conservative explicit budget or return NEEDS_INPUT. Never silently drop constraints, approvals, source provenance, failed attempts, or user decisions.
</unknowns_gate>
<execute>Apply KEEP / SUMMARIZE / DROP / RETRIEVE:
Checkpoint at phase boundaries or before compaction. Route work by capability requirements such as reasoning depth, tool use, latency, cost, multimodality, and context capacity; runtime policy selects the implementation. Keep stable prompt prefixes unchanged when caching is available.
Maintain one versioned shared-intent surface for objective, acceptance criteria, decisions, and non-code constraints. Give each worker a private context manifest containing only its required slice. Return summary, artifact locator/hash, decision, unknowns, and verifier receipt; do not treat chat replay as durable coordination. An independent critic receives the claim and raw artifact, not the builder's desired conclusion.
For Graph workflows, treat each node context as private RAM and each typed edge payload as a bounded transfer contract. Pass artifact locators, schemas, decisions, and verifier receipts; do not concatenate all branch histories into every successor. A join loads only the declared inputs needed for its decision.
</execute> <evaluate>Simulate the next action using only the checkpoint. It must recover objective, constraints, state, evidence, unknowns, last error, and next action without consulting lost chat. Compare estimated budget with runtime telemetry and correct drift.
</evaluate><state_contract>
Persist {run_id, status, attempt, budget, evidence, unknowns, last_error, next_action} plus phase, shared-intent hash, worker context manifests, decisions, permissions, normalized termination reason, active resources, completed work, rejected approaches, artifact/retrieval pointers, and budget telemetry. Version checkpoints; never replace the only recoverable state.
</state_contract>
NEEDS_INPUT: no reliable limit or cost policy exists and the choice changes execution.INSUFFICIENT_EVIDENCE: a proposed summary cannot preserve a material decision or locator; keep the source segment.VERIFY_FAILED: checkpoint replay loses required state; restore the prior version and recompact.BUDGET_STOP: reserve enough capacity for receipt and handoff, persist state, then stop.Return status, result (budget plan or checkpoint), evidence (runtime telemetry and replay check), unknowns, and next_action with required retrieval pointers.
</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.