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…
Skill by tjboudreaux
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: testing
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/thinking-thought-experiment
What's inside
- Skill name
thinking-thought-experiment- Description
- 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.
- License
- MIT
- Instructions
- 49 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# 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.
From tjboudreaux/cc-thinking-skills, MIT.
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Signals
- Stars
- 1,668
- Forks
- 185
- Open issues
- 2
- Last commit (this folder)
- Aug 4, 2026 (3 months ago)
- Last release
- Aug 4, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from tjboudreaux
- 0.782score
thinking-via-negativa
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Use when the reflex is to add a feature, layer, or process.
SkillBFreshness: B★ 1.7KClaude Code +9 - 0.782score
thinking-bounded-rationality
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Use when search or investigation could run forever.
SkillBFreshness: B★ 1.7KClaude Code +9 - 0.782score
thinking-circle-of-competence
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Use when a specific claim may lack grounding.
SkillBFreshness: B★ 1.7KClaude Code +9 - 0.782score
thinking-kepner-tregoe
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Use when a selective defect needs IS/IS-NOT difference analysis or a consequential option choice needs must/want weighting and adverse-consequence comparison.
SkillBFreshness: B★ 1.7KClaude Code +9 - 0.782score
thinking-lindy-effect
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Use when longevity of a non-perishable option matters.
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thinking-probabilistic
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thinking-thought-experiment skill. Agent Mart. https://agentmart.dev/skills/tjboudreaux-thinking-thought-experiment (last updated 2026-10-24)