pymoo
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
82
77%
Does it follow best practices?
Impact
93%
2.06xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/pymoo/SKILL.mdQuality
Discovery
82%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong, technically specific description that clearly identifies the domain and lists concrete algorithms, techniques, and benchmarks. Its main weakness is the lack of an explicit 'Use when...' clause, which would help Claude know exactly when to select this skill. The trigger terms are excellent for the target audience of engineers and optimization researchers.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about multi-objective optimization, evolutionary algorithms, Pareto-optimal solutions, or trade-off analysis in engineering design.'
Consider adding a few more natural-language trigger phrases like 'trade-off analysis', 'evolutionary algorithm', or 'multi-criteria decision making' to capture users who may describe the problem without using specific algorithm names.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and algorithms: NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, and specific benchmarks (ZDT, DTLZ). These are concrete, identifiable techniques rather than vague language. | 3 / 3 |
Completeness | The 'what' is well-covered (multi-objective optimization with specific algorithms and benchmarks), but there is no explicit 'Use when...' clause or equivalent trigger guidance. The mention of 'engineering design and optimization problems' partially implies when, but it's not an explicit trigger statement. Per rubric guidelines, missing 'Use when...' caps completeness at 2. | 2 / 3 |
Trigger Term Quality | Includes strong natural keywords that users in this domain would actually use: 'multi-objective optimization', 'NSGA-II', 'NSGA-III', 'MOEA/D', 'Pareto fronts', 'constraint handling', 'ZDT', 'DTLZ', 'engineering design'. These are the exact terms someone working on optimization problems would mention. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with very specific algorithm names (NSGA-II, NSGA-III, MOEA/D) and benchmark suites (ZDT, DTLZ) that are unique to multi-objective optimization. Extremely unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with excellent actionability and progressive disclosure. The code examples are concrete and executable across all major use cases. The main weaknesses are moderate verbosity (explaining concepts Claude already knows, like optimization problem types) and lack of validation/verification checkpoints in the workflows to confirm solution quality or convergence.
Suggestions
Remove or significantly trim the 'When to Use This Skill' and 'Core Concepts > Problem Types' sections, as Claude already understands these optimization concepts.
Add explicit validation checkpoints to workflows, such as checking convergence (e.g., comparing result.F across generations), verifying feasibility (result.CV), and confirming Pareto front quality before proceeding to decision making.
Condense the 'Performance and Troubleshooting' and 'Best practices' sections - much of this is general optimization knowledge that Claude already possesses.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some unnecessary sections like 'When to Use This Skill' (Claude can infer this), 'Core Concepts' explaining what single/multi/many-objective means, and verbose 'Best practices' that are general optimization knowledge. The algorithm selection tables and workflow examples are valuable but the overall document is longer than needed. | 2 / 3 |
Actionability | Excellent actionability with fully executable, copy-paste ready code examples for every workflow. Custom problem definitions include both constrained and unconstrained variants with clear constraint formulation rules. Algorithm configuration, operator customization, and visualization all have concrete, runnable code. | 3 / 3 |
Workflow Clarity | Workflows are clearly numbered and sequenced with good step-by-step structure. However, there are no validation checkpoints or feedback loops - no steps to verify solution quality, check convergence, or validate that constraints are satisfied before proceeding. For optimization workflows that can silently produce poor results, explicit verification steps (e.g., checking convergence metrics, validating feasibility) would be important. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure with a clear main document providing quick-start workflows and concise examples, with well-signaled one-level-deep references to detailed documentation (references/*.md) and executable scripts (scripts/*.py). The Resources section clearly catalogs all supplementary files with descriptions and search patterns. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (570 lines); consider splitting into references/ and linking | Warning |
metadata_version | 'metadata.version' is missing | Warning |
Total | 9 / 11 Passed | |
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
b58ad7e
- Reviewed
Table of Contents
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