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* feat(constrained-optimization): add formulation and audit methodology * docs(constrained-optimization): register skill and align trigger metadata * docs(constrained-optimization): route templates and define completion * docs(constrained-optimization): cite solver evidence and applicability
34 lines
1.5 KiB
Markdown
34 lines
1.5 KiB
Markdown
# Constrained optimization
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Make optimization decisions that remain valid when constraints, tradeoffs, and solver limits matter.
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## Why Install This Skill
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Optimization results are easy to misread: a lower score may violate a hard limit, a timeout may be mistaken for infeasibility, and an average from one random run may not survive repetition. This skill gives an agent a disciplined way to state the problem before choosing a method.
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It produces reviewable formulation and solution-audit records. The workflow separates feasible candidates from infeasible ones, preserves exact-solver bounds and gaps, and makes multiobjective and stochastic tradeoffs explicit.
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## What You Get
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| Directory | Purpose |
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| `SKILL.md` | Decision workflow, boundaries, and routing |
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| `references/` | Formulation, solver evidence, Pareto, robustness guidance, and primary-source index |
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| `templates/` | Formulation and solution-audit records |
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| `evals/` | Quality cases for common optimization failures |
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## Quick Start
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Start with `SKILL.md`, then fill `templates/formulation-record.md` before selecting a solver. Use `templates/solution-audit.md` to independently verify the returned candidate.
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## Triggers
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- Formulating a constrained optimization problem
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- Comparing exact methods, heuristics, or stochastic search
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- Reviewing feasibility, optimality gaps, or timeout claims
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- Choosing among Pareto tradeoffs or objective priorities
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## Requirements
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No solver, API key, or runtime is required. Named solver operation and statistical modeling remain with their respective skills.
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