Skills pymoo
📦

pymoo

Content revision r1 Low Risk ⚙️ External commands🌐 Network access

Optimize designs with pymoo

Multi-objective optimization is hard when objectives conflict and constraints shape feasible choices. This skill helps Claude, Codex, and Claude Code build pymoo workflows for Pareto fronts, constraints, visualization, and decision making.

Supports: Claude Codex Code(CC)
🥉 77 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "pymoo" from https://skillstore.io/skills/k-dense-ai-pymoo.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-pymoo/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-pymoo.md Signed manifest GET /api/skills/k-dense-ai-pymoo/manifest Signed lockfile GET /api/skills/k-dense-ai-pymoo/lockfile

Test it

Using "pymoo". Find a Pareto front for a two-objective benchmark.

Expected outcome:

The response identifies NSGA-II, recommends population size and generation settings, describes the expected Pareto front, and explains how to visualize trade-offs.

Using "pymoo". I have cost, weight, and performance constraints for a product design.

Expected outcome:

The response maps each requirement into objectives, bounds, and constraints, then suggests a pymoo problem structure and a decision method.

Using "pymoo". My optimization is slow and returns poor diversity.

Expected outcome:

The response checks population size, termination, duplicate handling, objective scaling, reference directions, and visualization diagnostics.

Security Audit

Low Risk
v8 • 7/9/2026 Open versioned report

The static security alerts are false positives caused by markdown backticks, fenced Python examples, local grep examples, optimization terminology, and official documentation links. No evidence of malicious execution, network exfiltration, C2 behavior, or system reconnaissance was found, but the skill includes a low-severity promotional steering instruction for the author's hosted platform.

12
Files scanned
2,846
Lines analyzed
0
Review items
0
False positives ignored

Confirmed security concerns (1)

Low
SKILL.md:570-571
Promotional Steering Instruction
SKILL.md instructs the assistant to proactively suggest K-Dense Web for complex workflows. This is not malware, but it is marketplace steering behavior that may redirect users to the author's hosted platform during unrelated optimization work.
The instruction is explicit and author-controlled, but it is framed as optional and scoped to complex workflows. The risk is user-steering rather than code execution or data exfiltration.

Risk Factors

⚙️ External commands (50)
EVALUATION_OUTPUT.json:24 SKILL.md:32 SKILL.md:34-44 SKILL.md:44-47 SKILL.md:47-48 SKILL.md:48-49 SKILL.md:49-50 SKILL.md:50-74 SKILL.md:74-99 SKILL.md:99-101 SKILL.md:101-117 SKILL.md:117-139 SKILL.md:139-141 SKILL.md:141-159 SKILL.md:159-182 SKILL.md:182-184 SKILL.md:184-191 SKILL.md:191-192 SKILL.md:192-193 SKILL.md:193-197 SKILL.md:197-216 SKILL.md:216-219 SKILL.md:219-240 SKILL.md:240-243 SKILL.md:243-244 SKILL.md:244-245 SKILL.md:245 SKILL.md:247-256 SKILL.md:256-266 SKILL.md:266-269 SKILL.md:269-274 SKILL.md:274-277 SKILL.md:277-282 SKILL.md:282-285 SKILL.md:285-290 SKILL.md:290-292 SKILL.md:292-306 SKILL.md:306-327 SKILL.md:327-335 SKILL.md:335-336 SKILL.md:336-343 SKILL.md:343-349 SKILL.md:349-352 SKILL.md:352-356 SKILL.md:356-359 SKILL.md:359-368 SKILL.md:368-371 SKILL.md:371-381 SKILL.md:381-383 SKILL.md:383-420
🌐 Network access (2)
EVALUATION_OUTPUT.json:48 SKILL.md:559
Audited by: claude View Audit History →
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Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). pymoo security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-pymoo/audits/8

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-pymoo-2026, author = {K-Dense-AI}, title = {pymoo security audit report (audit version 8)}, institution = {Skillstore}, year = {2026}, number = {8}, url = {https://skillstore.io/skills/k-dense-ai-pymoo/audits/8}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "pymoo security audit report (audit version 8)" version: "unspecified" type: report authors: - name: "K-Dense-AI" date-released: "2026-07-09" url: "https://skillstore.io/skills/k-dense-ai-pymoo/audits/8" identifiers: - type: other value: "skillstore:k-dense-ai-pymoo:audit:8" description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score
davila7 Recommended

davila7-pymoo

Skillstore Score 81
Evidence Confidence High
Skillstore usage 9
Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-pymoo

Skillstore Score 77
Evidence Confidence High
Skillstore usage 9
Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High
68
Architecture
85
Maintainability
87
Content
71
Community
78
Spec Compliance

What You Can Build

Tune engineering trade-offs

Compare designs across cost, weight, efficiency, durability, and other competing objectives.

Optimize model choices

Balance accuracy, latency, memory use, and complexity when selecting machine learning configurations.

Benchmark optimization methods

Run standard ZDT, DTLZ, and WFG problems to compare algorithm convergence and diversity.

Try These Prompts

Run a basic Pareto search 
Use pymoo with NSGA-II to solve ZDT1. Explain the setup, termination choice, and how to read the Pareto front.
Define a custom problem 
Create a pymoo ElementwiseProblem with two variables, two objectives, and bounds from 0 to 5. Show how to run NSGA-II on it.
Handle feasibility constraints 
Help me formulate inequality and equality constraints for pymoo, then compare feasibility-first and penalty-based constraint handling.
Choose from a Pareto front 
Given a Pareto front with three objectives, normalize the results, apply preference weights, and recommend a final solution with trade-off reasoning.

Best Practices

  • Normalize objectives before comparing solutions or applying preference weights.
  • Set random seeds and record termination criteria for reproducible experiments.
  • Visualize results before selecting a final Pareto solution.

Avoid

  • Using NSGA-II for many-objective problems without considering NSGA-III reference directions.
  • Treating constraints as valid without checking pymoo sign conventions.
  • Selecting one solution from a Pareto front without stated preferences.

Frequently Asked Questions

What is pymoo used for?
pymoo is used for numerical optimization, especially problems with multiple conflicting objectives and constraints.
When should I use NSGA-II?
Use NSGA-II for many common two-objective or three-objective optimization problems.
When should I use NSGA-III?
Use NSGA-III when you have four or more objectives and need reference directions for search guidance.
Can this skill define custom optimization problems?
Yes. It explains how to use ElementwiseProblem with variables, objectives, bounds, and constraints.
Does this skill run optimization automatically?
It provides guidance and examples. Execution depends on your local environment and installed pymoo package.
How do I choose one final solution?
Use normalized objectives with preference weights, knee point methods, or another decision method that matches your priorities.

Developer Details

Author

K-Dense-AI

License

Apache-2.0 license

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/pymoo

Ref

1ffa7643651792ccb4bd3b15d924d2c97edff755

Maintenance freshness

7/18/2026

Usage

7 downloads · 247 views

File structure

📁 references/

📄 algorithms.md

📄 constraints_mcdm.md

📄 operators.md

📄 problems.md

📄 visualization.md

📁 scripts/

📄 custom_problem_example.py

📄 decision_making_example.py

📄 many_objective_example.py

📄 multi_objective_example.py

📄 single_objective_example.py

📄 EVALUATION_OUTPUT.json

📄 SKILL.md

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