Thought Experiment
SkillDev toolsWhen 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.
Available today. Use it from your connected AI after setup.
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Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Thought Experiment skill
What this skill tells your AI
The instructions your AI receives, as published by tjboudreaux/cc-thinking-skills in skills/thinking-thought-experiment/SKILL.md and read by ahel’s review.
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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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 testisolated_variable: single change vs control worldinitial_conditions: frozen state and non-changesconsequence_chain: ordered mechanistic stepsinvariants: what still holdsbreak_points: first requirement failures and critical assumptionsimplication_bound: actions/checks justified by the chain onlydiscriminating_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.
Signals
- GitHub stars
- 1k
- Forks
- 158
- Last commit
- Aug 2026
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thinking-thought-experiment- Source
- github.com/tjboudreaux/cc-thinking-skills