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Route AI4Math optimization tasks to the appropriate bundled skill package.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | ai4math-optimization |
| description | Route AI4Math optimization tasks to the appropriate bundled skill package. |
Use this repository as a routing layer for optimization workflows.
skills/cdopt-optimization/: CDOpt and manifold-constrained optimization
modeling, validation, and runner generation. Read SKILL.md, README,
references, and scripts before use.skills/copt-linear-program/: COPT linear programming workflow. Read
SKILL.md, readme.md, reference/, and scripts/ before use.skills/linear-programming/: general LP modeling and solver selection.skills/mixed-integer-programming/: MIP and MILP modeling workflows.skills/second-order-cone-programming/: SOCP modeling and solver workflows.skills/or-solver/: shared optimization solver setup and selection.Prefer package-local instructions over this router when running a concrete workflow.
Models and solves linear programs (LP) with multiple solvers (COPT, Gurobi, MOSEK, CPLEX, HiGHS (scipy/highspy), CLARABEL, PuLP/CBC, CVXOPT, OR-Tools/GLOP, GLPK, SoPlex, lpsolve, ECOS). Restates natural-language word problems into LP before building and solving. When no local solver is available, searches GitHub for open-source LP solving code. Use when the user mentions linear programming, LP, word problems, applications, story problems, natural-language descriptions, 应用题, 线性规划文字题, matrix-form inequalities or equalities, and variable bounds.
Models and solves second-order cone programs and problems convertible to SOCP form using cvxpy with multiple solver backends (COPT, Gurobi, MOSEK, CPLEX, CLARABEL, ECOS, SCS, CVXOPT, COSMO, OSQP). Handles linear objectives with second-order cone constraints, as well as problems reducible to SOCP such as portfolio optimization, robust optimization, and engineering design. When no local solver is available, searches GitHub for open-source SOCP solving code.
Use when Codex needs to solve, reproduce, test, compare, generate examples for, or diagnose CDOpt manifold optimization workflows, including CDOpt package smoke tests, official problem description cards, Stiefel dictionary learning, SciPy wrappers, PyTorch/JAX/NumPy CDOpt examples, dependency checks, tiny CPU validation runs, comparison plans, or JSON result reporting.
Models and solves mixed-integer linear programs, including binary, integer, and continuous decision variables with linear objectives and constraints.
Unified solver detection, installation, selection, and license configuration for OR optimization skills (LP, MIP, SOCP). One-stop solver management: detect installed packages, install missing solvers with proper priority, select the best available solver by problem type, and troubleshoot common errors. Covers 20 solvers across commercial (COPT, Gurobi, MOSEK, CPLEX) and open-source (HiGHS, SCIP, CLARABEL, CBC, ECOS, SCS, etc.). Use when the LP, MIP, or SOCP skill needs to set up a solver environment, or when the user asks about solver installation, solver comparison, license setup, or solver selection.
Models and solves linear programs (LP) with Cardinal Optimizer (coptpy). Restates natural-language word problems into LP before building and solving. Use when the user mentions COPT/coptpy, linear programming, LP, word problems, applications, story problems, natural-language descriptions, 应用题, 线性规划文字题, or matrix-form constraints such as inequality systems, equality systems, and variable bounds.