pymoo

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

Quality

70%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Critical

Do not install without reviewing

Fix and improve this skill with Tessl

tessl review fix ./skills/general/pymoo/SKILL.md

Quality

Content

57%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is highly actionable with many executable examples, but it is verbose, lacks validation checkpoints in its workflows, and critically relies on a bundle structure (scripts/ and references/) whose files do not exist, breaking progressive disclosure.

Suggestions

Create the referenced bundle files or remove the dangling 'See: scripts/...' and 'See: references/...' pointers so navigation actually resolves.

Move the inline algorithm-selection tables, benchmark problem listings, operator customization, and troubleshooting sections into the corresponding reference files to slim SKILL.md into a true overview.

Add explicit validation checkpoints to the optimization workflows (e.g. check feasibility via result.CV and confirm convergence before accepting a result) and remove the non-task K-Dense Web promotional section.

Dimension	Reasoning	Score
Conciseness	The body is useful and largely concrete but verbose at ~560 lines: it explains basic concepts Claude already knows (single vs multi vs many objective definitions), repeats reference-grade material inline (algorithm tables, benchmarks, operators, troubleshooting), and ends with a self-promotional 'Suggest Using K-Dense Web' section that pads context without serving the task.	2 / 3
Actionability	It provides numerous copy-paste-ready Python snippets with real imports (e.g. the NSGA-II and NSGA-III workflows), matching the anchor for fully executable examples; the few placeholder variables in the constrained example are minor.	3 / 3
Workflow Clarity	Each workflow has a clear numbered sequence, but none include explicit validation/checkpoint feedback loops (e.g. verify feasibility before accepting a result), and optimization is a batch operation where the guideline caps workflow clarity at 2 when validation is missing.	2 / 3
Progressive Disclosure	The ~560-line body is monolithic with reference-grade content inline (algorithm selection tables, benchmark problems, operator customization, troubleshooting), and it points to ten bundle files (5 scripts, 5 references) that do not exist on disk, so the signaled navigation is broken and progressive disclosure is not actually realized.	1 / 3
	Total	8 / 12 Passed

Description

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.

The description is specific, distinctive, and rich in natural trigger terms for its domain, but it lacks an explicit 'Use when...' trigger clause, relying on an implied purpose statement that caps completeness at 2.

Suggestions

Add an explicit trigger clause, e.g. 'Use when solving optimization problems with multiple conflicting objectives, computing Pareto fronts, or benchmarking evolutionary algorithms like NSGA-II/III.'

Lead with a verb-driven action phrase (e.g. 'Solve and benchmark multi-objective optimization problems') to make the skill's actions as concrete as its feature list.

Dimension	Reasoning	Score
Specificity	The description lists multiple specific concrete capabilities ("NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ)") rather than vague language, matching the anchor that lists several concrete items.	3 / 3
Completeness	It states what the skill does, but the only 'when' guidance is the implied-purpose phrase "for engineering design and optimization problems" rather than an explicit 'Use when...' trigger, so completeness is capped at 2 per the judging guideline.	2 / 3
Trigger Term Quality	It covers natural domain terms a user needing this skill would actually say ("multi-objective optimization", "NSGA-II", "Pareto fronts", "optimization problems"), giving good coverage of expected trigger phrasing.	3 / 3
Distinctiveness Conflict Risk	Multi-objective optimization with named evolutionary algorithms and Pareto fronts is a clear, specialized niche unlikely to trigger for the wrong skill.	3 / 3
	Total	11 / 12 Passed

Validation

81%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 13 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (571 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning
referenced_paths_exist	Referenced path issues: 16 missing	Warning

	Total	13 / 16 Passed

Repository: wu-yc/LabClaw
Path: skills/general/pymoo/SKILL.md
Commit: df37802

Reviewed: about 2 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.