Use when designing or refactoring a model-native engineering workflow with bounded autonomy, probes, custom evaluation, durable state, and verified write-back.
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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.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
<skill_contract> <input>A mission with independently ownable workstreams, time SLA requirements, pre-flight task list, token budget, interfaces, permissions, and an integration owner.</input> <output>An isolated worker fleet (5 to 100+ agents), typed IPC ledger, pre-allocated token ledger, serial integration, cleanup, and evidence receipts.</output> <done>Pre-flight task list is 100% reconciled, integrated artifacts pass mission checks, token consumption is within TCLR boundaries, and cleanup gates pass.</done> <non_goals>Parallelism without a task list, unbounded token burn, overlapping writers, or worker self-certification.</non_goals>
Strategic intent and final integration remain serial; owned execution runs in dynamically scaled parallel squadrons (5, 10, 50, 100+ agents). Use the Ender lens for commander intent and squad autonomy, Palantir for operational objects/actions, and von Neumann for executable process architecture. Detailed patterns: references/ender-palantir-command-patterns.md; examples: references/classic-campaigns.md.
Provide: mission, time SLA, task list candidate, workstreams, scaling tier (5/10/50/100+), dependencies, acceptance criteria, token budget (ETC limit), permissions, runtime capabilities, and integration owner.
<unknowns_gate>
If dependencies, write ownership, or verification commands are ambiguous, return NEEDS_INPUT or INSUFFICIENT_EVIDENCE. Do not launch parallel workers before pre-flight task list and token ledger are fully reconciled.
</unknowns_gate>
<execute>context_manifest containing only its objective, dependencies, territory, inputs, output schema, budget, and verifier.pending -> active -> review -> accepted -> closed) to shared command board.For high-risk operations or cross-repo modifications, require independent review and human approval. Prepare a rollback plan before applying writes.
</execute> <evaluate>Audit total token consumption against the mission-declared TCLR: verified tasks resolved per 10,000 effective tokens consumed. Higher is better; do not invent a universal threshold. Verify all workstreams reached accepted or closed state, no file ownership conflicts occurred, and the declared regression suite passed.
<retry_policy>
max_attempts: 2. Stop on repeated failure signature or NO_PROGRESS. If a worker stalls or fails, reassign territory or downgrade to serial execution rather than launching unbudgeted retries.
</retry_policy>
<state_contract>
Persist {mission_id, run_id, status, attempt, budget, token_ledger, task_list, worker_fleet, context_manifest, command_board, evidence, unknowns, next_action} in durable storage.
</state_contract>
NEEDS_INPUT: Ambiguous mission scope or overlapping territory; prompt for clarification before launch.INSUFFICIENT_EVIDENCE: Missing verification command or unverified worker output; reject merge.BLOCKED_PERMISSION: Worker lacks required tool or file write permission; escalate to commander.BLOCKED_DEPENDENCY: Upstream task failed; suspend dependent workers until resolved.VERIFY_FAILED: Artifact fails independent evaluation; reject and trigger bounded repair.NO_PROGRESS: Worker produces duplicate errors after 2 attempts; stop worker and flag for review.BUDGET_STOP: Token consumption exceeds allocated ETC budget; trigger automatic halt. max_attempts: 2.Return status, result (worker fleet summary, task resolution ledger, and verified artifacts), evidence (source receipts, lint receipts, and integration logs), unknowns, and next_action including approval or rollback when relevant.
BUDGET_STOP kill.</skill_contract>
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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.
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.