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experiment-tracking

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Sets up disciplined ML experiment tracking - run logging schemas, artifact versioning, naming conventions, and reproducibility standards - and produces the run-record template a team actually follows. Use when someone asks "how should we track our ML experiments", "why can't we reproduce this result", "how do I set up MLflow or W&B for the team", or is bootstrapping a new ML project. Do NOT use for analyzing product A/B tests - use ab-test-analyzer instead; for monitoring deployed models use data-drift-monitor; for judging whether a trained model is good enough to ship use model-evaluation-report.

skills/experiment-tracking/SKILL.md

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# Experiment Tracking

An experiment no one can reproduce is a result no one can trust. Disciplined tracking is not overhead - it is the minimum viable scientific practice for ML, and the alternative is a promoted model whose provenance nobody can reconstruct when it misbehaves in production. This skill installs the logging schema, naming discipline, and promotion workflow that make every run auditable.

## Operating procedure

Follow the steps in order: the run schema (Step 2) must exist before naming conventions (Step 3) matter, and reproducibility standards (Step 4) are unenforceable until the schema captures seeds and environments.

### Step 1: Gather inputs

Collect these before touching tooling. Where the answer is unknown, record the default and label it a guess.

1. Team size and collaboration need. Default: solo or small team.
2. Existing tooling, if any (MLflow, Weights and Biases, homegrown spreadsheets).
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