Install Thinking Probabilistic
Pick your agent. Every command is ready to paste.
npx skills add tjboudreaux/cc-thinking-skills --skill thinking-probabilisticThe open skills CLI detects your agents (Claude Code, Cursor, Codex and 70+ more) and installs to the right folder. Add -g to install globally.
01
Install it
Run the command above, or upload the folder in Claude.
02
Ask naturally
Describe the task. Your agent loads thinking-probabilistic when the request matches its description.
03
Name it if needed
If it does not trigger, say “use the thinking-probabilistic skill”. In Codex, type $thinking-probabilistic.
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
- Define a checkable claim: outcome + timeframe + unit. Prefer a falsifiable statement over vague language ("likely").
- 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%).
- 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.
- 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.
- 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.
- 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
- Claim — falsifiable statement with timeframe.
- Prior — base rate, alternative path, adjustment reason, strongest countercase, and resulting prior probability.
- Range — confidence intervals (not a lone point).
- Updates — each evidence row: prior, LR (or explicit heuristic Δ), posterior.
- Fermi bounds (if used) — factor product and "~X within N×".
- 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.
Tips for Thinking Probabilistic
- • Start a fresh session after installing so your agent picks up the new skill.
- • It is part of a pack with 28 skills. Install only the ones you need with --skill to keep triggers precise.
- • If two skills overlap, mention the one you want by name.
Questions about Thinking Probabilistic
What does the Thinking Probabilistic 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.
How do I install Thinking Probabilistic?
Run "npx skills add tjboudreaux/cc-thinking-skills --skill thinking-probabilistic" in your project. The skills CLI installs it for Claude Code, Cursor, Codex and 70+ other agents. In Claude on the web, upload the skill folder as a ZIP under Customize > Skills.
Does Thinking Probabilistic work in Cursor and Codex?
Yes. Thinking Probabilistic uses the open SKILL.md format, so it works in Claude Code, Claude, Cursor, OpenAI Codex, Gemini CLI, GitHub Copilot and other agents that support agent skills.
Is Thinking Probabilistic free?
Thinking Probabilistic is open source on GitHub under the MIT license. It is free to install; check the license before commercial use.
Who made Thinking Probabilistic?
Thinking Probabilistic is published by tjboudreaux in the tjboudreaux/cc-thinking-skills repository, together with 27 other skills.




