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Thinking Thought Experiment

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tjboudreaux
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.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-thought-experiment
Stars
1.3K
Forks
158
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by tjboudreaux on GitHub. Read the source before you install it.

Installation

Install the Thinking Thought Experiment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tjboudreaux/cc-thinking-skills.git /tmp/cc-thinking-skills
mkdir -p .claude/skills
cp -r /tmp/cc-thinking-skills/skills/thinking-thought-experiment .claude/skills/thinking-thought-experiment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Thought Experiment in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Thinking Thought Experiment on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Thinking Thought Experiment is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Frequently asked questions

What does the Thinking Thought Experiment AI skill do?

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.

Why use Thinking Thought Experiment on TypingMind?

Because you install it once and use it with any model. Thinking Thought Experiment is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Thinking Thought Experiment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tjboudreaux/cc-thinking-skills/tree/main/skills/thinking-thought-experiment. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Thinking Thought Experiment?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Thinking Thought Experiment?

As many as you like. As long as a model supports skills, you can use Thinking Thought Experiment with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Thinking Thought Experiment AI skill free?

Yes. It is published on GitHub by tjboudreaux under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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