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 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.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
<skill_contract> <input>Source-grounded material, original question, learning or decision goal, audience, and destination.</input> <output>An eight-part evidence-aware mental model separating facts, mechanisms, conflicts, hypotheses, decisions, and action.</output> <done>Each material claim retains provenance and uncertainty, and one bounded next action answers the original question.</done> <non_goals>Inventing missing claims, erasing contradictions or provenance, or treating interpretation as source fact.</non_goals>
Compile information into a decision-ready mental model. Compression must preserve provenance, uncertainty, contradiction, and the original question.
Provide: source material, original question, intended decision or learning goal, and preferred destination. Optional: existing wiki concepts to compare.
Identify the source boundary, original question, audience, and required fidelity. Label direct source claims, external facts, and analyst inferences before synthesis.
</intake><unknowns_gate>
If the source is inaccessible, truncated, or missing the material needed to answer the question, return INSUFFICIENT_EVIDENCE. Ask one probe only when its answer changes the compile structure; otherwise proceed with explicit unknowns.
</unknowns_gate>
<execute>Produce eight compact sections:
Prefer one durable concept note over several thin notes. Preserve source links or block references; do not rewrite immutable source notes.
</execute> <evaluate>Verify that every material conclusion traces to evidence or is labeled inference; the original question is answered; contradictions remain visible; and the action follows from the model. Remove summaries that merely restate the source without changing understanding.
</evaluate>NEEDS_INPUT: the intended question or audience is absent and materially changes the output.INSUFFICIENT_EVIDENCE: the source cannot support the requested compile.VERIFY_FAILED: a conclusion lacks traceable evidence; downgrade it to hypothesis or remove it.Return status, result (the eight-section compile), evidence (source locators), unknowns, and next_action.
</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 a long-running agent task needs context budgeting, checkpointing, compaction, retrieval, or capability-based model routing.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.