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Review a notebook or analysis for data-science anti-patterns before it's trusted or shared.
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Basado en la clasificación ocupacional SOC
Establish a dumb baseline and an honest validation harness before any real model, so every later number means something.
Rigor for causal questions and A/B tests (the effect of acting on X): confounding, post-treatment bias, valid control groups.
Rigor for descriptive and diagnostic analytics (what happened and why): denominators, grain, and confounded slices, not model leakage.
EDA kickoff for a fresh dataset: fixed opening scaffold (goal, imports, load, sanity), then chapters derived from the data; scan leakage, land a baseline.
Rigor for statistical inference (is the difference real): hypothesis tests, power, multiple comparisons, effect size over p-value.
Audit a dataset or pipeline for the five leakages that inflate a metric: target, preprocessing, temporal, group, and sampling.
| name | lemma-review |
| description | Review a notebook or analysis for data-science anti-patterns before it's trusted or shared. |
| homepage | https://github.com/tkpratardan/lemma |
| license | MIT |
Return findings ranked by severity, with concrete evidence, consequence, and a specific correction. Distinguish correctness defects from optional improvements.
Do not rewrite the analysis merely for style, accept a clean-looking notebook as proof, or report only aggregate metrics when a material group failure is visible.
For detailed review patterns, read references/deep-guide.md.
