Guides the creation of rigorous, well-controlled experiments for machine learning and data science projects.
The Experiment Design Coach skill provides a systematic framework for planning scientific inquiries within the Claude Code environment. It helps users define clear hypotheses, identify essential control variables, and establish primary and secondary metrics to ensure experimental results are reproducible and statistically significant. By assisting with compute budget calculations and warning against common pitfalls like insufficient replicates or metric mismatches, this skill ensures your research and development efforts are both resource-efficient and methodologically sound.
Key Features
010 GitHub stars
02Structured hypothesis formulation with independent and dependent variables
03Detailed control variable management including seeds and data splits
04Comprehensive markdown experiment plan generation with success criteria
05Compute budget estimation for GPU-hours and total run time
06Multi-tier metric definition for performance, stability, and throughput
Use Cases
01Establishing rigorous testing protocols for evaluating model performance across data splits
02Planning ablation studies to test neural network architecture changes
03Designing hyperparameter optimization experiments with clear baselines
