ml-experiment-standards
Always invoke for training, validating, tuning, benchmarking, or claiming readiness of a predictive model. Covers leakage audits, spatial and grouped splits, metrics, reproducibility, and honest reporting. Invoke especially when spatial dependence, split design, or deployment geography is unknown; uncertainty is a reason to use this skill. Do not trigger for descriptive EDA or non-predictive statistical inference.
Install
With your agent
Install OMGSkills skill "muend/geoai-skills:skills/ml-experiment-standards" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/muend/geoai-skills /tmp/geoai-skills && ln -s /tmp/geoai-skills/skills/ml-experiment-standards ~/.claude/skills/ml-experiment-standardsFrom README
ML Experiment Standards Purpose: every ML job (quick prototypes included) is reproducible, leakage-free, and metric-justified. These are not optional polish; every skipped item typically returns as "the model collapsed in production" or "the result didn't replicate". EDA comes first Before any model, produce and show: distributions, missingness rates, outliers, target balance, salient correlations. Metric and loss choice depend on this information; a model recommendation without EDA is a guess.
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