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

66%

Does it follow best practices?

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Securityby

Passed

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Fix and improve this skill with Tessl

tessl review fix ./backend/cli/skills/coding/pymoo/SKILL.md

Quality

Content

67%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.

The body is a well-organized, highly actionable skill with seven sequenced workflows, real bundle files for every referenced path, and clear navigation to reference material. Its main weaknesses are moderate redundancy and inline content that overlaps with the provided reference files.

Suggestions

Remove the Resources section's duplicate file listing (or the inline "See:" links) so each reference is pointed to once, tightening conciseness.

Make the constrained custom-problem example fully executable by defining f1/f2/g1/g2/h1 instead of leaving placeholders.

Move the inline Algorithm Selection, Benchmark Problems, and Operator Customization tables to their existing reference files, keeping only a compact pointer inline to improve progressive disclosure.

Dimension	Reasoning	Score
Conciseness	Mostly efficient and code-first, but includes redundancy such as the Resources section re-listing references/scripts already linked inline and a Common patterns section that repeats constraint/NSGA-III rules stated earlier.	3 / 5
Actionability	Seven workflows ship concrete, mostly copy-paste-ready Python covering the common cases, with only minor gaps such as the constrained custom-problem snippet using undefined f1/f2/g1/g2 placeholders.	4 / 5
Workflow Clarity	Each workflow has a clear When/Steps/Example sequence and a troubleshooting section supplies error-recovery guidance, though inline validation checkpoints within workflows are only implicit.	4 / 5
Progressive Disclosure	Good structure with well-signaled one-level-deep references to references/.md and scripts/.py (all referenced files exist), though the body keeps substantial inline algorithm/benchmark/operator content that duplicates dedicated reference files.	4 / 5
	Total	15 / 20 Passed

Description

66%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.

The description is specific and well-stocked with distinctive algorithm names, giving it strong trigger-term and distinctiveness qualities, but it lacks an explicit "Use when..." trigger clause so completeness is capped at 3. It is concise and not padded.

Suggestions

Add an explicit "Use when..." trigger clause (e.g., "Use when solving multi-objective or constrained optimization, finding Pareto fronts, or benchmarking evolutionary algorithms") to raise completeness.

Include a few natural synonyms users say ("evolutionary algorithms", "genetic algorithms", "trade-offs") to broaden trigger-term coverage.

Mention visualization and decision-making capabilities to close the minor specificity gaps.

Dimension	Reasoning	Score
Specificity	Lists several concrete capabilities ("NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ)") with minor gaps (visualization, decision making, custom operators are absent).	4 / 5
Completeness	Has a clear "what" (the framework and its algorithms) but the "when" is only weakly implied by "for engineering design and optimization problems" with no explicit "Use when..." trigger clause, capping completeness at 3 per the guidelines.	3 / 5
Trigger Term Quality	Good coverage of natural optimization terms ("Multi-objective optimization", "Pareto fronts", "NSGA-II", "optimization problems") a user would say, though synonyms like "evolutionary algorithms" or "trade-offs" are missing.	4 / 5
Distinctiveness Conflict Risk	Mostly distinct niche anchored by named algorithms (NSGA-II/III, MOEA/D, Pareto fronts), with minor overlap risk against general optimization tools due to the broad "engineering design and optimization problems" phrasing.	4 / 5
	Total	15 / 20 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
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	13 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/coding/pymoo/SKILL.md
Commit: a820c2f

Reviewed: 3 days ago

Table of Contents

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