optimization-modeling
When the user wants to build optimization models, solve mathematical programming problems, or find optimal solutions. Also use when the user mentions "linear programming," "integer programming," "mixed-integer programming," "constraint optimization," "mathematical optimization," "operations research," "optimal solution," or "decision optimization." For network design specifically, see network-design. For routing, see route-optimization.
npx skills add majiayu000/claude-skill-registry --skill optimization-modeling --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
You are an expert in mathematical optimization and operations research for supply chain. Your goal is to help formulate and solve optimization problems that find the best decisions subject to constraints, minimizing costs or maximizing profits and service levels.
How it works
The skill guides the agent through an initial assessment of the business problem, decision variables, constraints, data availability, and technical environment. It then presents concrete problem types:
- Linear Programming (LP): continuous variables, linear objective and constraints, with an example of production planning using PuLP, including variables, objective, constraints, solving, and results extraction.
- Integer Programming / Mixed-Integer Programming (IP/MIP): binary and/or discrete variables, with a facility location example including fixed costs, capacity, transportation, and constraints, solved via PULP_CBC_CMD, with extraction of open facilities and flows.
- Nonlinear Programming (NLP): nonlinear objective/constraints, continuous variables, with an inventory optimization example using scipy.optimize.minimize, detailing structure of the objective and budget constraint, bounds, and result extraction.
When to use it
Triggered when the user is dealing with optimization modeling in supply chain contexts such as production planning, transportation, facility location, inventory optimization, and related decision optimization tasks (linear programming, integer programming, mixed-integer programming, constraint optimization, or operations research). The skill explicitly mentions these problem types and their applications.
What it can touch
The skill references tools and libraries used in the examples: "PuLP" (with LpProblem, LpVariable, LpMinimize, etc.), and "scipy.optimize" (minimize). It shows usage with file-like code blocks and function definitions to build and solve models. It also shows data structures like dictionaries for demands, capacities, costs, and resource usage. It does not specify external data sources beyond the code examples.
Caveats
The skill provides sample code snippets and example data structures. It does not include licensing changes beyond the MIT license in the metadata. It emphasizes standard optimization formulations and common solver interfaces, with no guarantees about specific outcomes or solver performance. And it remains focused on the optimization modeling domain and its concrete, step-by-step formulations.
# Optimization Modeling You are an expert in mathematical optimization and operations research for supply chain. Your goal is to help formulate and solve optimization problems that find the best decisions subject to constraints, minimizing costs or maximizing profits and service levels. ## Initial Assessment Before building optimization models, understand: 1. **Business Problem** - What decision needs to be made? (production, inventory, routing, scheduling) - What's being optimized? (minimize cost, maximize profit, maximize service) - Time horizon? (operational, tactical, strategic) - Expected impact and value? 2. **Decision Variables** - What can be controlled? (quantities, assignments, schedules) - Continuous or discrete decisions? - Scale? (10 variables vs. 100,000 variables) 3. **Constraints** - What limits exist? (capacity, budget, time, demand requirements) - Hard constraints (must satisfy) vs. soft constraints (preferences)? - How many constraints? (dozens vs. millions) 4. **Data Availability** - All parameters known with certainty? - Uncertainty or variability? - Data quality and completeness? 5. **Technical Environment** - Optimization expertise in team? - Solver access? (
- Initial Assessment
- Optimization Problem Types
- Linear Programming (LP)
- Integer Programming (IP) / Mixed-Integer Programming (MIP)
- Nonlinear Programming (NLP)
- Advanced Optimization Techniques
- Multi-Objective Optimization
- Stochastic Optimization
- Robust Optimization
- Decomposition Methods
- Benders Decomposition
- Column Generation
- Tools & Solvers
- Open-Source Solvers
What does the optimization-modeling skill do?
When the user wants to build optimization models, solve mathematical programming problems, or find optimal solutions. Also use when the user mentions "linear programming," "integer programming," "mixed-integer programming," "constraint optimization," "mathematical optimization," "operations research," "optimal solution," or "decision optimization." For network design specifically, see network-design. For routing, see route-optimization.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill optimization-modeling --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.