Model constraint satisfaction problems as MiniZinc programs with solver guidance.
npx skills add https://github.com/Cogni-AI-OU/cogni-ai-agent-skills --skill minizinc
Please help me install this Agent Skill. Skill: minizinc Source: https://github.com/Cogni-AI-OU/cogni-ai-agent-skills/tree/main/minizinc Command: npx skills add https://github.com/Cogni-AI-OU/cogni-ai-agent-skills --skill minizinc
MiniZinc is used to formulate constraint satisfaction and combinatorial optimization problems so they can be solved reliably and efficiently instead of relying on ad-hoc, error-prone reasoning.
Common use cases include scheduling, assignment, grid/matrix constraint modeling, and building optimized combinatorial solvers where model quality strongly impacts runtime.
Ask the AI to produce a MiniZinc model (using globals.mzn) that solves your specified scheduling or assignment constraints with tight domains, symmetry breaking, and an output that presents human-readable decision variable values.
High-intent search queries and answers about installing and using this skill.
Constraint satisfaction problems are modeled by declaratively defining variables, tight domains, and global constraints, separating instance data from model structure to ensure reliable and efficient solving instead of ad-hoc reasoning.
Scheduling and assignment constraints are best structured using global-first constraint composition, reusable predicates, symmetry breaking, and explicit search annotations to improve solver propagation and runtime performance.
SAT and UNSAT outcomes are verified through a disciplined debugging loop using targeted tracing, invariants, and performance-focused refinement to validate the correctness of the constraint model.
Solver performance for combinatorial models is optimized by applying tight domains, symmetry breaking techniques, global constraints, and explicit search annotations to maximize propagation efficiency.
Separating instance data from model structure via parameters is required for constraint solving to build reusable, scalable models that can handle varying datasets without altering the underlying logic.
Grid and matrix constraint modeling benefits from channeling techniques, global constraints, reusable primitives like predicates and functions, and tight domain declarations to ensure correct and efficient solving.
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