simulation-experiment-designer
Simulation experimental design skill for efficient scenario analysis and optimization.
npx skills add a5c-ai/babysitter --skill simulation-experiment-designer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# simulation-experiment-designer You are **simulation-experiment-designer** - a specialized skill for designing and analyzing simulation experiments efficiently. ## Overview This skill enables AI-powered simulation experimentation including: - Factorial experiment design for simulation - Latin hypercube sampling - Variance reduction techniques (common random numbers, antithetic variates) - Ranking and selection procedures - Metamodel fitting (response surface) - OptQuest-style simulation optimization - Scenario comparison with statistical tests ## Prerequisites - Python 3.8+ with pyDOE2, SALib, scipy - SimPy or other DES framework - Statistical analysis libraries ## Capabilities ### 1. Factorial Experiment Design ```python import pyDOE2 as doe import numpy as np def create_factorial_design(factors, levels=2): """ Create full or fractional factorial design factors: dict of {name: (low, high)} """ n_factors = len(factors) factor_names = list(factors.keys()) if levels == 2: # Full factorial design_coded = doe.ff2n(n_factors) # Fractional factorial for many factors if n_factors > 5: design_coded = doe.fracfact( ' '.join(['a', 'b', 'c', 'd', 'e'][:n_factors]) ) else: # General full fact
- Overview
- Prerequisites
- Capabilities
- 1. Factorial Experiment Design
- 2. Latin Hypercube Sampling
- 3. Variance Reduction - Common Random Numbers
- 4. Ranking and Selection
- 5. Response Surface Metamodel
- 6. Sensitivity Analysis
- Process Integration
- Output Format
- Tools/Libraries
- Best Practices
- Constraints
What does the simulation-experiment-designer skill do?
Simulation experimental design skill for efficient scenario analysis and optimization.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill simulation-experiment-designer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.