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.
Fetches pymoo from davila7/claude-code-templates and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
.cursor/skills/pymoo
Restart Cursor to activate pymoo. Access via /pymoo in your agent's command palette.
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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)
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
Quick Start Workflows
Workflow 1: Single-Objective Optimization
When: Optimizing one objective function
Steps:
Define or select problem
Choose single-objective algorithm (GA, DE, PSO, CMA-ES)
Configure termination criteria
Run optimization
Extract best solution
Example:
from pymoo.algorithms.soo.nonconvex.ga import GA
from pymoo.problems import get_problem
from pymoo.optimize import minimize
# Built-in problemproblem = get_problem("rastrigin", n_var=10)# Configure Genetic Algorithmalgorithm = GA( pop_size=100, eliminate_duplicates=True)# Optimizeresult = minimize( problem, algorithm,('n_gen',200), seed=1, verbose=True)print(f"Best solution: {result.X}")print(f"Best objective: {result.F[0]}")
See:scripts/single_objective_example.py for complete example
Algorithm choice: NSGA-III (designed for many objectives)
Key difference: Must provide reference directions for population guidance
Steps:
Define many-objective problem
Generate reference directions
Configure NSGA-III with reference directions
Run optimization
Visualize using Parallel Coordinate Plot
Example:
from pymoo.algorithms.moo.nsga3 import NSGA3
from pymoo.problems import get_problem
from pymoo.optimize import minimize
from pymoo.util.ref_dirs import get_reference_directions
from pymoo.visualization.pcp import PCP
# Many-objective problem (5 objectives)problem = get_problem("dtlz2", n_obj=5)# Generate reference directions (required for NSGA-III)ref_dirs = get_reference_directions("das-dennis", n_dim=5, n_partitions=12)# Configure NSGA-IIIalgorithm = NSGA3(ref_dirs=ref_dirs)# Optimizeresult = minimize(problem, algorithm,('n_gen',300), seed=1)# Visualize with Parallel Coordinatesplot = PCP(labels=[f"f{i+1}"for i inrange(5)])plot.add(result.F, alpha=0.3)plot.show()
See:scripts/many_objective_example.py for complete example
Workflow 4: Custom Problem Definition
When: Solving domain-specific optimization problem
Steps:
Extend ElementwiseProblem class
Define __init__ with problem dimensions and bounds
Implement _evaluate method for objectives (and constraints)
Use with any algorithm
Unconstrained example:
from pymoo.core.problem import ElementwiseProblem
import numpy as np
classMyProblem(ElementwiseProblem):def__init__(self):super().__init__( n_var=2,# Number of variables n_obj=2,# Number of objectives xl=np.array([0,0]),# Lower bounds xu=np.array([5,5])# Upper bounds)def_evaluate(self, x, out,*args,**kwargs):# Define objectives f1 = x[0]**2+ x[1]**2 f2 =(x[0]-1)**2+(x[1]-1)**2 out["F"]=[f1, f2]
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
