본문으로 건너뛰기
cccskills
무료GitHub에 공개

thinking-thought-experiment

When a real test is too rare, large, or irreversible, run a controlled counterfactual: isolate one variable, fix conditions, trace the mechanistic chain, and bound what the result implies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md3.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Thought Experiment

When empiricism is out of reach, run a disciplined counterfactual: one isolated change, fixed conditions, step-by-step mechanism, and a hard bound on implications.

When to Use

  • You need behavior under failure, scale, or policy you cannot cheaply trigger or measure (region outage, 100x load, one-way architecture).
  • A decision is expensive or irreversible and a mental trace can surface break points before commit.
  • Edge cases are too costly to stage, but a mechanistic chain can still expose missing controls.

When NOT to Use

  • A cheap real test exists (load test, flag, query, spike) → run the test; do not substitute imagination.
  • Adversarial security attack-path work → use red-team structure, not free-form scenarios.
  • You already know the mechanism and only need a decision under known facts → decide; do not dramatize.
  • Vague "what if everything" brainstorming without a single isolated variable → tighten or stop.

Procedure

  1. State the question and isolation. Name exactly one primary variable or counterfactual change. Freeze all other conditions as the control world. Reject multi-variable "and also" scenarios.
  2. Fix initial conditions. Specify system state, load, configuration, actors, and what is not changed. Write values concrete enough that another agent could replay the setup.
  3. Trace the mechanism step by step. From t0, record what fails, queues, retries, or adapts next—and why—using known components and policies only. No hand-wavy "then everything collapses"; each step needs a causal link.
  4. Extract invariants and break points. Note what still holds (invariants) and the first step where the system violates a requirement (capacity, correctness, safety, UX). Mark assumptions that, if false, void the chain.
  5. Bound implications. Map insights only to actions or checks justified by the chain (limits, guards, monitoring, redesign). Label speculative leaps beyond the isolation as out of bound.
  6. Name a discriminating real check, then stop. For the weakest link, state the cheapest observation or experiment that would confirm or kill it. Stop after one controlled chain with bounded implications; if a link is cheaply testable now, exit to that test instead of further imagination.

Output

Emit a thought-experiment record:

  • question: what behavior or decision is under test
  • isolated_variable: single change vs control world
  • initial_conditions: frozen state and non-changes
  • consequence_chain: ordered mechanistic steps
  • invariants: what still holds
  • break_points: first requirement failures and critical assumptions
  • implication_bound: actions/checks justified by the chain only
  • discriminating_check: cheapest real observation to confirm or kill the weak link

Verification

  • Isolation check: more than one free variable without a stated control → invalid; reset.
  • Mechanism check: any step without a causal link to a known component/policy → rewrite or drop.
  • Implication bound: recommendations not entailed by the chain are out of scope.
  • Empiricism override: if a real test became available mid-analysis, stop the thought experiment and test.
  • Over-application guard: do not use this skill for ordinary debugging you can reproduce, or as a substitute for red-team threat modeling.
  • Stop: one isolated counterfactual → full chain → bounded implications + discriminating check; no scenario sprawl.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Use when search or investigation could run forever. Set an explicit good-enough threshold first, then stop at the first option that clears it.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

tjboudreaux/cc-thinking-skills1.7천2026년 8월 8일 업데이트

Use when a specific claim may lack grounding. Check evidence boundary, size wrongness cost, then answer, fetch, or abstain — never confabulate.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

tjboudreaux/cc-thinking-skills1.7천2026년 8월 8일 업데이트

When the right response mode is unclear, classify the cause-effect domain first; decompose disorder.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

tjboudreaux/cc-thinking-skills1.7천2026년 8월 8일 업데이트

Under genuine uncertainty with no reliable forecast, inventory means, cap downside at affordable loss, act for commitments, and let goals emerge from controllable moves.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

tjboudreaux/cc-thinking-skills1.7천2026년 8월 8일 업데이트

When a constraint is treated as fixed, separate physics from convention, keep only independently supported primitives, and rebuild the simplest solution that satisfies real constraints.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

tjboudreaux/cc-thinking-skills1.7천2026년 8월 8일 업데이트

When a fault is localized and the proximate cause is known but the systemic root is not, chain evidence-linked whys with a counterfactual stop and a countermeasure.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

tjboudreaux/cc-thinking-skills1.7천2026년 8월 8일 업데이트

tjboudreaux의 스킬 모두 보기

이 스킬의 문제 신고