---
title: "optimization-toolkit"
description: "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."
canonical: https://agentpluginsdirectory.com/plugins/optimization-toolkit
last-updated: 2026-10-12
---

# 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.
- Slug: optimization-toolkit
- Publisher: Moheet Subudhi
- Repository: https://github.com/moheetsubudhi-isb/business-analytics-skills
- Manifest: plugins/optimization-toolkit/plugin.json
- Version: 1.0.0
- License: MIT
- Category (editorial): other
- Skills: 5 (exact-vs-heuristic, logical-constraints, optimization-formulation, or-model-test-plan, shortage-allocation-fairness)
- MCP servers: 0
- Stars: 0
- Repository created: 2026-09-13
- Repository last pushed: 2026-10-07
- Publisher type: User
- Listing: https://agentpluginsdirectory.com/plugins/optimization-toolkit
- Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json

## What optimization-toolkit does, in the publisher's words

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.

- 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.

From the project README, punctuation lightly normalized. Full text: https://raw.githubusercontent.com/moheetsubudhi-isb/business-analytics-skills/HEAD/plugins/optimization-toolkit/README.md