tjboudreaux/cc-thinking-skills

thinking-kepner-tregoe

Use when a selective defect needs IS/IS-NOT difference analysis or a consequential option choice needs must/want weighting and adverse-consequence comparison.

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/tjboudreaux/cc-thinking-skills --skill thinking-kepner-tregoe

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

Kepner-Tregoe Analysis

Core rule: Diagnose deviations by testing causes against both IS and IS-NOT. Compare consequential choices by screening MUSTs, weighting WANTs, and exposing adverse consequences before selecting.

When to Use

  • A defect affects some objects, places, times, or cohorts but not comparable others.
  • Several candidate causes remain and the contrast boundary can discriminate them.
  • A consequential option choice has explicit non-negotiables, competing objectives, and risks that should be compared consistently.

When NOT to Use

  • A uniform failure has no meaningful IS-NOT contrast, or the cause is already confirmed.
  • One cheap observation settles the cause or one option plainly dominates every requirement.
  • The criteria cannot be made operational; clarify them before assigning weights.
  • The task is forward failure discovery for a planned change rather than diagnosis or option selection.

Procedure

  1. Choose the mode. Use Problem Analysis for a deviation from expected behavior; use Decision Analysis for a choice among options. State the target and do not mix scores with causal evidence.
  2. Frame the target. For a deviation, record object, defect, location, time, extent, and impact. For a choice, state the decision, alternatives, constraints, and deadline.
  3. Problem Analysis — build IS/IS-NOT. For WHAT, WHERE, WHEN, and EXTENT, record IS, closest comparable IS-NOT, and the distinction unique to the IS side. List changes near the first occurrence.
  4. Problem Analysis — difference-test causes. Generate candidates from distinctions and changes. A candidate survives only if it explains both IS and IS-NOT. Run the cheapest discriminating check; stop when one verified cause explains the full boundary.
  5. Decision Analysis — screen and score. Define pass/fail MUSTs and weighted WANTs (1–10 importance) before scoring. Eliminate options that fail any MUST; score survivors against each WANT and calculate weighted totals using the same scale.
  6. Decision Analysis — test downside and sensitivity. For leading options, list adverse consequences with probability × impact and identify assumptions or weight changes that would reverse the ranking. Do not let a high total conceal a ruinous failure mode.
  7. Decide or expose the gap. Return the verified cause or highest-ranked acceptable option, the evidence/score behind it, residual risk, and next verification. If no cause verifies or no option passes MUSTs, return open/none rather than force a winner.

Output

Return one mode-specific decision artifact:

  • Problem Analysis: problem statement; IS/IS-NOT matrix with distinctions; nearby changes; candidate-vs-boundary tests; confirmed cause or next discriminating check.
  • Decision Analysis: decision statement; alternatives; MUST screen; weighted WANT matrix; adverse-consequence table; sensitivity/reversal conditions; selected option or none.

Verification

  • Falsify/stop: reject a cause that cannot explain both sides of the boundary. Reject a choice if it fails a MUST, depends on inconsistent scoring, or loses under a plausible weight/risk change that was hidden.
  • Over-application guard: skip the full matrix for an obvious cause, trivial choice, or one-shot check. Stop when the cause verifies or the option is robust enough for the stated stakes; extra rows are ceremony.
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