菜单
Assemble a trustworthy working dataset from messy or multiple sources: grain, keys, joins with match rates, extraction checks, lineage.
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基于 SOC 职业分类
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-wrangle |
| description | Assemble a trustworthy working dataset from messy or multiple sources: grain, keys, joins with match rates, extraction checks, lineage. |
| homepage | https://github.com/tkpratardan/lemma |
| license | MIT |
Produce the requested analytical dataset or result with its output grain, key/definition contract, join or extraction diagnostics, lineage, and unresolved conflicts.
scripts/source_inventory.py is available for complex inventories.Do not enter this skill merely because several compatible files are used. Do not silently coerce units or keys, permit an unexplained many-to-many join, or drop unmatched records without accounting for them.
For authority matrices and extraction QA, read references/deep-guide.md.