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Thinking Probabilistic

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tjboudreaux
thinking-probabilistic

Use when forecasting, estimating, or sizing risk — anchor on base rates, give ranges, update prior→likelihood→posterior on evidence, and factor unmeasured quantities into order-of-magnitude bounds.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-probabilistic
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 Probabilistic 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-probabilistic .claude/skills/thinking-probabilistic
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Probabilistic 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 Probabilistic 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 Probabilistic 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.

Probabilistic Thinking

Core rule: State beliefs as numbers and ranges, not vibes. Anchor on a base rate, update with an explicit likelihood, and bound unknowns by factoring them — never invent false precision.

When to Use

  • Timeline, effort, or outcome forecasts where the true value is uncertain.
  • Risk sizing for a change, migration, deploy, or launch.
  • Any moment you are about to state a confident single number you cannot actually know.
  • New evidence arrives and a prior estimate should move.

When NOT to Use

  • The quantity is measurable or look-up-able — measure or look it up.
  • The decision is invariant across the whole plausible range — skip the estimate and act.
  • There is no real reference class and you would invent a base rate — label it a guess, not a calibrated forecast.
  • You only need a binary gate and already have a decisive observation — do not pad with ceremony.

Procedure

  1. Define a checkable claim: outcome + timeframe + unit. Prefer a falsifiable statement over vague language ("likely").
  2. Lock a prior and challenge it: name a reference-class base rate and at least one credible alternative path/hypothesis with its rate. Pull the prior toward the base rate unless you write a concrete reason for deviation. Then state the strongest evidence-based case that your chosen prior or range is wrong, what estimate it supports, and revise if that countercase survives. Convert vague words to numbers (e.g. "likely" ≈ 65–80%).
  3. Express a range, not a point: give at least one confidence interval (50% and 80% preferred). Assume overconfidence; widen intervals when the outside view is thin.
  4. Update prior → likelihood → posterior when evidence arrives:
    • Prior odds = p / (1 − p).
    • Likelihood ratio LR = P(E|H) / P(E|¬H). LR > 1 supports H; LR = 1 is noise; LR < 1 undermines H.
    • Posterior odds = prior odds × LR (multiply even when LR < 1); p = odds / (1 + odds).
    • Strength bands for distance from 1: weak ~1.5–3×, moderate 3–10×, strong 10–100×, definitive 100×+.
    • Yesterday's posterior is today's prior for the next evidence. For rare events, start from the base rate — vivid positives still leave most mass on false alarms.
  5. Fermi-bound unmeasured quantities (only when you need a magnitude you cannot measure/look up):
    • Decompose: Quantity = Factor₁ × Factor₂ × … (or sum of components).
    • Bound each factor with a range; use one-significant-figure geometric means for order-of-magnitude.
    • Multiply; report "~X within 3–5×"; sanity-check whether a 10× error would change the decision; replace any factor that is actually lookup-able.
    • Skip Fermi when the number is cheaply measurable, when the decision needs tighter than ~3–5× precision, or when every factor is pure invention.
  6. State the final estimate for checking: claim, range/CIs, key uncertainties, and the observation that would prove it wrong. Stop when the decision is stable across the remaining range or the next update needs new evidence you do not have.

Output

  1. Claim — falsifiable statement with timeframe.
  2. Prior — base rate, alternative path, adjustment reason, strongest countercase, and resulting prior probability.
  3. Range — confidence intervals (not a lone point).
  4. Updates — each evidence row: prior, LR (or explicit heuristic Δ), posterior.
  5. Fermi bounds (if used) — factor product and "~X within N×".
  6. Decision implication — what changes if the true value is at the low vs high end of the range.

Verification

  • Falsify/stop: if you cannot name a base rate, alternative, or serious countercase, label the estimate as a guess rather than calibrated. If the decision is unchanged across the full range, stop estimating. If new evidence arrives and the number does not move (or moves without an LR/Δ), recompute.
  • Over-application guard: do not dress checkable facts as probabilities, invent reference classes, or Fermi-decompose quantities you can measure. Do not report three significant figures on a 5×-uncertain product. For rare events, refuse jumps from one vivid hit to near-certainty without the base-rate prior.

Frequently asked questions

What does the Thinking Probabilistic AI skill do?

Use when forecasting, estimating, or sizing risk — anchor on base rates, give ranges, update prior→likelihood→posterior on evidence, and factor unmeasured quantities into order-of-magnitude bounds.

Why use Thinking Probabilistic on TypingMind?

Because you install it once and use it with any model. Thinking Probabilistic 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 Probabilistic in TypingMind?

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

Which AI models can use Thinking Probabilistic?

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 Probabilistic?

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

Is the Thinking Probabilistic 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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