Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
8.7
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Machine Learning
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Exceptional skill documentation for multi-objective optimization with pymoo. The SKILL.md provides comprehensive, workflow-based coverage that enables a CLI agent to invoke optimization tasks confidently. Seven well-structured workflows cover the full spectrum from single-objective to many-objective problems, custom problem definition, constraint handling, decision making, and visualization. Each workflow includes clear 'when to use', step-by-step instructions, and working code examples. The structure is excellent: concise main document with clear indexing to detailed references and executable scripts. Task knowledge is outstanding with practical examples, algorithm selection guides, troubleshooting advice, and best practices. The skill addresses a genuinely complex domain where CLI agents would struggle with the nuances of algorithm selection, constraint formulation, reference direction generation, and multi-criteria decision making—tasks that would consume many tokens to solve from scratch. Minor improvement areas: could explicitly mention parallelization capabilities and provide more guidance on computational cost estimation for different problem scales. Overall, this is a highly polished, agent-ready skill that meaningfully reduces the complexity and token cost of optimization tasks.
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