Optimization toolkit

Decision-science skills for turning business decisions into optimisation models, writing business rules correctly, allocating scarce supply fairly, choosing exact solvers or heuristics, and testing models before go-live.

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 (5)

• exact-vs-heuristic: Choose between an exact solver and a heuristic for a hard planning problem, and set honest expectations on speed, plan quality and explainability.
• logical-constraints: Write yes/no business rules as correct linear constraints in a mixed-integer model, then prove them right by enumeration.
• optimization-formulation: Turn a business decision into an optimisation model and a recommendation a business owner can act on.
• or-model-test-plan: Plan how to test an optimisation model so its plans can be trusted in live operations.
• shortage-allocation-fairness: Decide who gets what when there is not enough to go round, and explain the trade-off between efficiency and fairness to the people affected.

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