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 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。
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>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.
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。
Use when a long-running agent task needs context budgeting, checkpointing, compaction, retrieval, or capability-based model routing.
中文简介正在准备中。当前显示原文说明。