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ASUKaiwenFang
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text-to-optimization

Convert natural-language optimization problems into schema-validated IR and Julia/JuMP models.

3|Updated Feb 14, 2026
Search Tags:#optimization#ir#modeling#deterministic#julia#jump#highs
One-click install
npx skills add https://github.com/ASUKaiwenFang/text-to-optimization --skill text-to-optimization
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: text-to-optimization
Source: https://github.com/ASUKaiwenFang/text-to-optimization/tree/main
Command: npx skills add https://github.com/ASUKaiwenFang/text-to-optimization --skill text-to-optimization

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires JSON3, JSONSchema, JuMP, HiGHS, MathOptInterface, PackageCompiler, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Converts natural-language optimization word problems into a validated intermediate representation and deterministic solver artifacts so models are reproducible, consistent, and ready to solve.

Core Features & Use Cases

  • Structured IR: extracts all numeric inputs as named parameters and encodes problems in a JSON schema-validated IR.
  • Deterministic exports: generates both a human-readable Markdown mathematical formulation and deterministic Julia/JuMP code.
  • Integrated solve pipeline: boots Julia, validates the IR, exports artifacts, runs HiGHS, and appends results to the Markdown; useful for production planning, resource allocation, and transportation models.

Quick Start

Ask the agent to "Maximize weekly profit given limited labor and machine hours" and the skill will produce a schema-validated IR, a Markdown formulation, deterministic Julia/JuMP code, and solver results.

Frequently Asked Questions about text-to-optimization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I convert natural language optimization problems into JuMP models?▼

Convert natural-language optimization problems into JuMP models by extracting inputs into a schema-validated JSON IR, then generating deterministic Julia/JuMP code and a Markdown mathematical formulation.

What types of optimization problems can I solve with JuMP and HiGHS?▼

JuMP and HiGHS solve linear and quadratic optimization workflows such as production planning, resource allocation, and transportation problems by running the generated deterministic code and appending results.

Can I generate a reproducible intermediate representation for linear programming models?▼

Generate a reproducible intermediate representation for linear programming by extracting all numeric inputs as named parameters and encoding the problem in a JSON schema-validated IR.

Does this optimization workflow support running the solver and exporting results automatically?▼

The integrated solve pipeline boots Julia, validates the IR, exports artifacts, runs HiGHS, and appends the solver results directly to the Markdown formulation.

What is the best way to formulate a resource allocation problem in Julia?▼

Formulate a resource allocation problem in Julia by asking the agent to process your word problem, which produces deterministic Julia/JuMP code and a human-readable Markdown mathematical formulation.

Do I need to manually write Julia code for transportation optimization models?▼

You do not need to manually write Julia code for transportation optimization models because the skill generates deterministic, solver-ready Julia/JuMP code from your natural-language problem description.

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