pymoo

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

2.06x

Quality

77%

Does it follow best practices?

Impact

93%

2.06x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/pymoo/SKILL.md

Evaluation results

95%

23%

Sustainable Vehicle Fleet Design Optimization

Many-objective optimization with NSGA-III and MCDM

Criteria

Without context

With context

NSGA-III algorithm

100%

Reference directions provided

100%

das-dennis method

100%

Correct n_dim for ref_dirs

100%

PCP visualization

100%

Objective normalization

100%

PseudoWeights for decision making

100%

Weights sum to 1

100%

Seed set

100%

ElementwiseProblem used

100%

Termination tuple syntax

100%

Result extraction

100%

Installation command

90%

41%

Heat Exchanger Design Optimization

Custom constrained problem definition and NSGA-II

Criteria

Without context

With context

ElementwiseProblem base class

100%

n_ieq_constr declared

100%

Inequality constraints as g <= 0

100%

out['G'] assignment

100%

NSGA-II for 2 objectives

100%

Seed set to 1

Feasibility check via CV

100%

Scatter visualization

100%

xl and xu defined

100%

Feasible solutions filtered

57%

100%

eliminate_duplicates set

Termination tuple syntax

100%

Result F and X extracted

100%

uv installation

100%

95%

79%

Job Shop Scheduling Optimization

Operator selection by variable type and convergence analysis

Criteria

Without context

With context

OrderCrossover for permutations

100%

InversionMutation for permutations

100%

PermutationRandomSampling

100%

save_history=True

100%

History accessed

100%

Convergence visualized or reported

100%

Seed set to 1

ElementwiseProblem base class

100%

GA used for single-objective

100%

Operators passed to algorithm

100%

uv installation

100%

Best solution extracted

100%

Repository: K-Dense-AI/claude-scientific-skills
Commit: b58ad7e

Evaluated: 27 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Sustainable Vehicle Fleet Design Optimization Heat Exchanger Design Optimization Job Shop Scheduling Optimization

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