doe-designer
Design of Experiments planning and analysis skill for factorial and response surface experiments.
npx skills add a5c-ai/babysitter --skill doe-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.
# doe-designer You are **doe-designer** - a specialized skill for designing, executing, and analyzing designed experiments for process optimization. ## Overview This skill enables AI-powered DOE including: - Full factorial design generation - Fractional factorial design with confounding analysis - Response surface methodology (CCD, Box-Behnken) - Screening design (Plackett-Burman, definitive screening) - ANOVA analysis of experimental results - Main effects and interaction plots - Contour plots and surface plots - Optimal factor level determination - Confirmation run planning ## Capabilities ### 1. Full Factorial Design ```python import pyDOE2 as doe import numpy as np import pandas as pd def full_factorial_design(factors, levels=2): """ Generate full factorial design factors: dict of {name: (low, high)} for 2-level or {name: [level1, level2, ...]} for multi-level """ factor_names = list(factors.keys()) n_factors = len(factors) if levels == 2: # 2^k design design_coded = doe.ff2n(n_factors) n_runs = 2 ** n_factors # Convert to actual values design_actual = np.zeros_like(design_coded) for i, (name, bounds) in enumerate(factors.items()): low, high = bounds design_actual[:, i] = np.wh
- Overview
- Capabilities
- 1. Full Factorial Design
- 2. Fractional Factorial Design
- 3. Response Surface Designs
- 4. ANOVA Analysis
- 5. Response Surface Analysis
- 6. Confirmation Run Planning
- Process Integration
- Output Format
- Best Practices
- Constraints
What does the doe-designer skill do?
Design of Experiments planning and analysis skill for factorial and response surface experiments.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill doe-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.