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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

78%

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Securityby

Passed

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

Quality

Content

80%Weight 40%Scale 1-5

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

A high-quality, actionable body with excellent progressive disclosure and copy-paste-ready examples. Its main gap is missing validation/feedback checkpoints inside the batch optimization workflows, which caps workflow clarity.

Suggestions

Add explicit validation checkpoints inside workflows (e.g., after minimize(): check result.CV for feasibility, verify len(result.F) and convergence against the true Pareto front before proceeding).

Trim repeated boilerplate imports and the basic problem-type definitions that Claude already knows to lift conciseness toward the top anchor.

Add a short feedback loop in the troubleshooting section (observe symptom -> diagnose -> adjust parameter -> re-run -> confirm) to formalize error recovery.

Dimension	Reasoning	Score
Conciseness	The body is dense with executable code and structured tables and largely avoids explaining concepts Claude already knows, though some import patterns and basic problem-type explanations repeat and could be trimmed.	4 / 5
Actionability	Seven workflows plus operator and benchmark sections provide fully executable, copy-paste-ready code (real imports, get_problem('zdt1'), NSGA2(pop_size=100)) covering the common cases comprehensively.	5 / 5
Workflow Clarity	Each workflow has clear numbered Steps sequences, but validation/verification checkpoints are absent within the optimization (batch) workflows, which per the rubric caps workflow clarity at 3.	3 / 5
Progressive Disclosure	SKILL.md is a clear overview with well-signaled, one-level-deep references to verified real files (algorithms.md, problems.md, operators.md, visualization.md, constraints_mcdm.md) and a scripts/ directory, giving easy navigation.	5 / 5
	Total	17 / 20 Passed

Description

70%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A specific, distinctive description with strong trigger keywords, but it lacks an explicit 'Use when...' trigger clause, which caps completeness. Adding concrete verb-driven actions and an explicit usage trigger would raise the top dimensions.

Suggestions

Add an explicit 'Use when ...' clause with concrete trigger phrases (e.g., 'Use when solving multi-objective or constrained optimization, computing Pareto fronts, or benchmarking evolutionary algorithms').

Lead with verb-driven actions (e.g., 'Runs NSGA-II/III, MOEA/D; computes Pareto fronts; handles constraints') rather than a bare noun list to lift specificity to the top anchor.

Include common synonyms/file markers a user might mention (e.g., 'evolutionary algorithms', '.py optimization') to round out trigger-term coverage.

Dimension	Reasoning	Score
Specificity	Enumerates multiple specific concrete capabilities ('NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ)') but as named items rather than verb-driven actions, leaving minor coverage gaps relative to the top anchor.	4 / 5
Completeness	The 'what' is clear and detailed, but there is no explicit 'Use when...' clause — 'for engineering design and optimization problems' only weakly implies the trigger, capping completeness per the missing-trigger guidance.	3 / 5
Trigger Term Quality	Strong natural keyword coverage with the terms a pymoo user would actually say ('multi-objective optimization', 'NSGA-II', 'Pareto fronts', 'engineering design and optimization problems'), though a few synonyms/extensions are absent.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (pymoo/multi-objective evolutionary optimization) anchored by specialized algorithm names, making it highly distinguishable with minimal conflict risk.	5 / 5
	Total	16 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (573 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning

	Total	14 / 16 Passed

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/pymoo/SKILL.md
Revision: 5ce2258

Reviewed: 27 days ago

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pymoo

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