القائمة
Review a notebook or analysis for data-science anti-patterns before it's trusted or shared.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.
