Plan and optimize multi-factor experiments using DOE templates.
npx skills add https://github.com/nahisaho/satori --skill scientific-doe
Please help me install this Agent Skill. Skill: scientific-doe Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-doe Command: npx skills add https://github.com/nahisaho/satori --skill scientific-doe
Experimental design is often resource-intensive and requires rigorous planning to identify influential factors efficiently.
Define factors and levels, run an initial screening with orthogonal arrays, and then refine the design using CCD or RSM templates.
High-intent search queries and answers about installing and using this skill.
Plan multi-factor experiments by defining factors and levels, running initial screening with orthogonal arrays, then refining the design using central composite or response surface templates.
Orthogonal arrays are used for initial factor screening to identify influential variables, while central composite designs are applied afterward to refine the design and optimize the response surface.
Yes, Box-Behnken and D-optimal designs are supported for process optimization and quantitative understanding across research domains including chemistry, biology, and materials science.
Use factorial design for initial screening to identify influential factors, then apply response surface methods when you need to refine the design and optimize the quantitative process.
Start by defining factors and levels, run an initial screening with orthogonal arrays to efficiently identify influential factors, then refine the design using response surface templates.
leifericfExecute a structured incident review to identify contributing factors and define action items.
Hongyu-yuPlan multi-factor experiments with design matrices and analysis strategies.
benchflow-aiQuantify relative factor contributions to a response variable using R² decomposition.
lyndonklDesign experiments to screen factors and map response surfaces.
