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From omer-metin-skills-for-antigravity-2
Provides formal Design of Experiments (DOE) methodology covering factorial designs, blocking, randomization, and optimal design strategies to maximize information from experiments while minimizing resources.
How this skill is triggered — by the user, by Claude, or both
Slash command
/omer-metin-skills-for-antigravity-2:experimental-designThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here.references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
npx claudepluginhub joshuarweaver/cascade-code-general-misc-2 --plugin omer-metin-skills-for-antigravity-2Generates structured experimental designs (factorial, response surface, Taguchi) to systematically discover how multiple factors affect outcomes while minimizing runs. Use for multi-factor optimization, screening, or parameter tuning.
Designs experiments and studies before data collection — selecting designs, randomizing, blocking, and generating treatment layouts for interpretable results.
Design experiments and studies from research questions. Triggers when user says: 'design experiment', 'study design', 'experimental setup', 'how should I test this', 'plan my study', 'ablation study', 'baseline comparison', 'research protocol', 'pilot study', 'sample size', 'how many participants do I need'. Generates comprehensive experiment designs including variables, sample sizing, protocols, and reproducibility checklists. Use this skill to plan how a hypothesis will be tested before any data is collected; choosing a test for data you have already collected is statistical-analysis.