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decision-tree-solver

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Turn a fork-in-the-road decision into a computed expected-value tree — settle or sue, launch or wait, fix or replace — rolled back by the bundled script, with the break-even probability where the answer flips. Use when asked should we settle or go to trial, build a decision tree, what probability makes this worth it, or compare options under uncertainty. Produces the structured tree, the rollback with the best choice at every fork, the break-even probabilities, and the honest list of what the numbers leave out. Decision support, not advice — the probabilities are yours.

skills/decision-tree-solver/SKILL.md

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Nov 10, 2026
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MIT

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# Decision Tree Solver

"Should we settle or go to trial" is not answered by instinct or by whoever argues longest — it is three numbers and a probability, and most people have never actually multiplied them. This skill extracts the tree hiding inside a messy decision (the choices, the chances, the payoffs, the costs of playing), computes the rollback with the bundled script, and — the part instinct can never do — finds the break-even: the probability at which the recommendation *flips*. Because "trial is worth it if you win 60% of the time" is an opinion, but "the answer flips at 90% — are you more than 90% sure?" is a decision made tractable. The script is deterministic, stdlib-only, and shows its arithmetic.

## What This Skill Produces

- **The extracted tree** — decisions, chance nodes with probabilities, outcomes with values, and the costs of each path, pulled from the situation as described
- **The rollback** — expected value at every node, the best choice named at every fork, from the script
- **The break-even scan** — for each two-way uncertainty, the probability at which the top-level choice flips, which is the number that makes probability arguments productive
- **The robustness read** — whether the answer survives the probabilities being argued about, or hinges on a number nobody can defend
- **The leaves-out list** — what expected value cannot see here: risk appetite, one-shot vs repeated, the unquantified costs

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