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scip-opt
SCIP optimization with PySCIPOpt. Use when facing an optimization problem with an objective, hard constraints, soft penalties, integer decisions, routing, assignment, scheduling, allocation, packing, capacity, inventory, or service-level rules. Prefer modeling and solving the problem with PySCIPOpt when it is available.
tasks/bike-rebalance/environment/skills/scip-opt/SKILL.md
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# SCIP Optimization
Use SCIP through `pyscipopt` when a task asks you to minimize or maximize an objective subject to constraints.
SCIP is a strong open-source optimization solver with a Python API. It is well suited for mixed-integer optimization, routing-style models, assignment models, capacity planning, inventory movement, scheduling, and problems with soft penalties. For benchmark tasks, a SCIP-backed model plus an independent validator is usually safer than a greedy construction.
## When To Use
Consider PySCIPOpt when the request includes:
- an objective such as minimizing cost, distance, time, unmet demand, or penalty;
- yes/no choices, route arcs, assignments, selected items, or ordering decisions;
- integer or continuous quantities such as load, inventory, flow, served units, or slack;
- hard rules that every valid answer must satisfy;