Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add foryourhealth111-pixel/Vibe-Skills --skill pymoogit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/pymoo)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/pymoo"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/pymoo/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/pymoo"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/pymoo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.04135 |
| Opus 5 | $0.00023 | $0.02067 |
| Sonnet 5 | $0.00009 | $0.00827 |
| Haiku 4.5 | $0.00005 | $0.00413 |
Grade A, and why
pymoo scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
94% identical to pymoo — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 573 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pymoo - Multi-Objective Optimization in Python
Routing Boundary
Use this skill only for pymoo, NSGA-II/NSGA, Pareto-front analysis, multi-objective optimization, constrained optimization, and pymoo algorithm implementation. Do not use it for generic optimization planning, experiment design, gradient descent, Bayesian modeling, PyMC, or causal analysis.
Overview
Pymoo is a comprehensive Python framework for optimization with emphasis on multi-objective problems. Solve single and multi-objective optimization using state-of-the-art algorithms (NSGA-II/III, MOEA/D), benchmark problems (ZDT, DTLZ), customizable genetic operators, and multi-criteria decision making methods. Excels at finding trade-off solutions (Pareto fronts) for problems with conflicting objectives.
When to Use This Skill
This skill should be used when:
- Solving optimization problems with one or multiple objectives
- Finding Pareto-optimal solutions and analyzing trade-offs
- Implementing evolutionary algorithms (GA, DE, PSO, NSGA-II/III)
- Working with constrained optimization problems
- Benchmarking algorithms on standard test problems (ZDT, DTLZ, WFG)
- Customizing genetic operators (crossover, mutation, selection)
- Visualizing high-dimensional optimization results
- Making decisions from multiple competing solutions
- Handling binary, discrete, continuous, or mixed-variable problems
Core Concepts
The Unified Interface
Pymoo uses a consistent minimize() function for all optimization tasks:
from pymoo.optimize import minimize
result = minimize(
problem, # What to optimize
algorithm, # How to optimize
termination, # When to stop
seed=1,
verbose=True
)
Result object contains:
result.X: Decision variables of optimal solution(s)result.F: Objective values of optimal solution(s)result.G: Constraint violations (if constrained)result.algorithm: Algorithm object with history
Problem Types
Single-objective: One objective to minimize/maximize Multi-objective: 2-3 conflicting objectives → Pareto front Many-objective: 4+ objectives → High-dimensional Pareto front Constrained: Objectives + inequality/equality constraints Dynamic: Time-varying objectives or constraints
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/algorithms.md 6.0 KB
- references/constraints_mcdm.md 11 KB
- references/operators.md 8.6 KB
- references/problems.md 6.9 KB
- references/visualization.md 9.0 KB
- scripts/custom_problem_example.py 4.7 KB runs code
- scripts/decision_making_example.py 4.5 KB runs code
- scripts/many_objective_example.py 2.1 KB runs code
- scripts/multi_objective_example.py 1.7 KB runs code
- scripts/single_objective_example.py 1.6 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 573 lines · 46 tokens per session scan A 5855d5a94258
pymoo is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,262 stars, last pushed 12d ago), licensed Apache-2.0. It adds 46 tokens to every session and 4,135 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to pymoo, differing in 5 lines, and is treated as a copy.
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