Guide AMPL and amplpy optimization answers with environment detection and response modes.
npx skills add https://github.com/Mihailby/amplskills --skill ampl
Please help me install this Agent Skill. Skill: ampl Source: https://github.com/Mihailby/amplskills/tree/main/skills Command: npx skills add https://github.com/Mihailby/amplskills --skill ampl
This Skill prevents vague or error-prone AMPL guidance by enforcing consistent environment detection, response modes, and formulation standards while grounding answers in AMPL documentation chunks.
data; blocks in amplpy).Ask your question naturally, for example: "In Jupyter, help me debug an infeasible AMPL model and refactor the formulation to improve numerical stability."
High-intent search queries and answers about installing and using this skill.
To debug an infeasible AMPL optimization model in Jupyter, you switch to Debug response mode to identify constraint conflicts and refactor the formulation to improve numerical stability using amplpy environment rules.
The best way to load data into amplpy is by using Python and pandas directly, explicitly avoiding AMPL `data;` blocks to maintain native script compatibility and prevent data-loading errors.
To avoid manual linearization in AMPL models, use native AMPL constructs such as abs, min, max, piecewise linear, and logical operators to prevent weak big-M formulations and improve solver performance.
Solver selection for AMPL optimization problems depends on the problem class, matching specific solvers to LP, MIP, NLP, MINLP, conic, global, or VRP categories while applying tuning and diagnosis hints.
Yes, you can use AMPL with Python scripts outside of Jupyter, as the environment detection mechanism routes responses for Pure AMPL, amplpy in Jupyter, and standard Python-script workflows separately.
Response modes for building and refactoring AMPL models include Full Build, Debug, Explanation, Refactor, Performance, and Data modes, which switch dynamically based on user intent and existing code presence.
Monjyu1101Run SimPO training scripts to align LLMs without a reference model.
mohamed-g-shoaibAnalyze and optimize PostgreSQL performance, replication, and maintenance tasks.
sverzijlSolve LP, MILP, NLP, MINLP, and DAE optimization models with Pyomo.
tondevrelSolve combinatorial optimization problems with Google OR-Tools and CP-SAT.