Use when optimizing multi-factor systems with limited experimental budget, screening many variables to find the vital few, discovering interactions between parameters, mapping response surfaces for peak performance, validating robustness to noise factors, or when users mention factorial designs, A/B/n testing, parameter tuning, process optimization, or experimental efficiency.
Plans efficient multi-factor experiments to maximize learning while minimizing runs. Use when optimizing systems with 3+ variables, screening many factors, or discovering interactions between parameters.
/plugin marketplace add lyndonkl/claude/plugin install lyndonkl-thinking-frameworks-skills@lyndonkl/claudeThis skill inherits all available tools. When active, it can use any tool Claude has access to.
resources/evaluators/rubric_design_of_experiments.jsonresources/methodology.mdresources/template.mdDesign of Experiments (DOE) helps you systematically discover how multiple factors affect an outcome while minimizing the number of experimental runs. Instead of testing one variable at a time (inefficient) or guessing randomly (unreliable), DOE uses structured experimental designs to:
Use this skill when:
Trigger phrases: "optimize", "tune parameters", "factorial test", "interaction effects", "response surface", "efficient experiments", "minimize runs", "robustness", "sensitivity analysis"
Design of Experiments is a statistical framework for planning, executing, and analyzing experiments where you deliberately vary multiple input factors to observe effects on output responses.
Quick example:
You're optimizing a web signup flow with 3 factors:
Naive approach: Test one at a time = 6 runs (2 levels each × 3 factors)
DOE approach: 2³ factorial design = 8 runs
Result: You discover that layout and CTA color interact strongly—multi-step + green outperforms everything, but single-page + blue is close second. Social proof has minimal effect. Make data-driven decision with confidence.
Copy this checklist and track your progress:
Design of Experiments Progress:
- [ ] Step 1: Define objectives and constraints
- [ ] Step 2: Identify factors, levels, and responses
- [ ] Step 3: Choose experimental design
- [ ] Step 4: Plan execution details
- [ ] Step 5: Create experiment plan document
- [ ] Step 6: Validate quality
Step 1: Define objectives and constraints
Clarify the experiment goal (screening vs optimization), response metric(s), experimental budget (max runs), time/cost constraints, and success criteria. See Common Patterns for typical objectives.
Step 2: Identify factors, levels, and responses
List all candidate factors (controllable inputs), specify levels for each factor (low/high or discrete values), categorize factors (control vs noise), and define response variables (measurable outputs). For screening many factors (8+), see resources/methodology.md for Plackett-Burman and fractional factorial approaches.
Step 3: Choose experimental design
Based on objective and constraints:
Step 4: Plan execution details
Specify randomization order (eliminate time trends), blocking strategy (control nuisance variables), replication plan (estimate error), sample size justification (power analysis), and measurement protocols. See Guardrails for critical requirements.
Step 5: Create experiment plan document
Create design-of-experiments.md with sections: objective, factors table, design matrix (run order with factor settings), response variables, execution protocol, and analysis plan. Use resources/template.md for structure.
Step 6: Validate quality
Self-assess using resources/evaluators/rubric_design_of_experiments.json. Check: objective clarity, factor completeness, design appropriateness, randomization plan, measurement protocol, statistical power, analysis plan, and deliverable quality. Minimum standard: Average score ≥ 3.5 before delivering.
Pattern 1: Screening (many factors → vital few)
Pattern 2: Optimization (find best settings)
Pattern 3: Response Surface (map the landscape)
Pattern 4: Robust Design (work despite noise)
Pattern 5: Sequential Experimentation (learn then refine)
Critical requirements:
Randomize run order: Eliminates time-order bias and confounding with lurking variables. Use random number generator, not "convenient" sequences.
Replicate center points: For designs with continuous factors, replicate center point runs (3-5 times) to estimate pure error and detect curvature.
Avoid confounding critical interactions: In fractional factorials, don't confound important 2-way interactions with main effects. Choose Resolution ≥ IV if interactions matter.
Check design balance: Ensure orthogonality (factors are uncorrelated in design matrix). Correlation > 0.3 reduces precision and interpretability.
Define response precisely: Use objective, quantitative, repeatable measurements. Avoid subjective scoring unless calibrated with multiple raters.
Justify sample size: Run power analysis to ensure design can detect meaningful effect sizes with acceptable Type II error risk (β ≤ 0.20).
Document assumptions: State expected effect magnitudes, interaction assumptions, noise variance estimates. Design validity depends on these.
Plan for analysis before running: Specify statistical tests, significance level (α), effect size metrics before data collection. Prevents p-hacking.
Common pitfalls:
Key resources:
Typical workflow time:
When to escalate:
Inputs required:
Outputs produced:
design-of-experiments.md: Complete experiment plan with design matrix, randomization, protocols, analysis approachCreating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.