# ml-experiment-standards

> Claude/Codex skill by [@muend](https://omgskills.com/library/muend/index.md) · 35 stars · Updated 2026-10-17

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.

Tags: agent-skills, ai, ai-agents, claude-code, codex, geoai, geospatial, gis, github-copilot, google-earth-engine, lidar, machine-learning, postgis, remote-sensing, spatial-analysis, spatial-data-science

## Install

```sh
git clone https://github.com/muend/geoai-skills /tmp/geoai-skills && ln -s /tmp/geoai-skills/skills/ml-experiment-standards ~/.claude/skills/ml-experiment-standards
```

## From 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.

## Links

- HTML page: https://omgskills.com/library/muend/geoai-skills/skills/ml-experiment-standards/
- GitHub: https://github.com/muend/geoai-skills
- Author: [@muend](https://omgskills.com/library/muend/index.md)

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