Decision analysis toolkit

Decision-analysis skills for structuring choices under uncertainty as decision trees, valuing information before paying for a test, survey or pilot, and designing Monte Carlo simulations for risk, inventory and schedule decisions.

Part of Business Analytics Skills, a set of agent skills that make an AI assistant work like a decision scientist and an advisor. Each skill asks what the work is for only when the answer would change the result, follows a real method step by step, runs a check wherever there is logic to verify, and hands back a plain-language brief plus a technical appendix.

Skills (3)

• decision-tree-analysis: Decide between options whose payoff depends on uncertain outcomes, using a decision tree and expected monetary value (EMV).
• simulation-model-design: Design, run and read a Monte Carlo simulation when a plan depends on several uncertain inputs.
• value-of-information: Decide whether to buy information before a decision: a market test, pilot, survey, trial run, inspection, diagnostic, consultant report or extra data.

Use

Skills load by themselves when a request matches. Ask a normal work question; to call one directly, type / and the skill name in Claude Code, Cursor or Copilot, or $ and the name in Codex.

What it runs

Instructions only, plus small optional Python checks (scripts/, standard library, numpy, pandas or scikit-learn) that read only the file you point them at. Nothing here reads credentials, environment secrets or your files on its own, makes network calls, or sends data anywhere.

Licence

MIT. Source and issues: https://github.com/moheetsubudhi-isb/business-analytics-skills