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lemma-wrangle

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Aktualisiert15. Juli 2026 um 00:47

Reconcile sources into a defensible analytical dataset when grain, keys, definitions, units, authority, extraction, joins, or provenance are uncertain.

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tkpratardan

tkpratardan/lemma

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Verwandte BerufeSOC

Basierend auf der SOC-Berufsklassifikation

DatenwissenschaftlerInformatik- und Mathematikberufe·SOC 15-2051

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3 Dateien

SKILL.md

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name	lemma-wrangle
description	Reconcile sources into a defensible analytical dataset when grain, keys, definitions, units, authority, extraction, joins, or provenance are uncertain.

Wrangle and reconcile

Deliver

Produce the requested analytical dataset or result with its output grain, key/definition contract, join or extraction diagnostics, lineage, and unresolved conflicts.

Check

Inventory source ownership, dates, formats, units, row grain, and likely keys. scripts/source_inventory.py is available for complex inventories.
Profile key uniqueness and missingness before joining.
Preserve raw fields; create documented normalized fields.
Measure match rates, unmatched records, row multiplication, duplication, and metric conservation for joins.
Validate parsed or transformed values against representative source examples.

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.

Mehr aus diesem Repository

gleiches Repository

lemma-baseline

tkpratardan/lemma

Establish a dumb baseline and an honest validation harness before any real model, so every later number means something.

2026-07-154

lemma-causal

tkpratardan/lemma

Rigor for causal questions and A/B tests (the effect of acting on X): confounding, post-treatment bias, valid control groups.

2026-07-154

lemma-describe

tkpratardan/lemma

Rigor for descriptive and diagnostic analytics (what happened and why): denominators, grain, and confounded slices, not model leakage.

2026-07-154

lemma-eda

tkpratardan/lemma

EDA kickoff for a fresh dataset: fixed opening scaffold (goal, imports, load, sanity), then chapters derived from the data; scan leakage, land a baseline.

2026-07-154

lemma-inference

tkpratardan/lemma

Rigor for statistical inference (is the difference real): hypothesis tests, power, multiple comparisons, effect size over p-value.

2026-07-154

lemma-leakage

tkpratardan/lemma

Audit a dataset or pipeline for the five leakages that inflate a metric: target, preprocessing, temporal, group, and sampling.

2026-07-154

name	lemma-wrangle
description	Reconcile sources into a defensible analytical dataset when grain, keys, definitions, units, authority, extraction, joins, or provenance are uncertain.

Wrangle and reconcile

Deliver

Produce the requested analytical dataset or result with its output grain, key/definition contract, join or extraction diagnostics, lineage, and unresolved conflicts.

Check

Inventory source ownership, dates, formats, units, row grain, and likely keys. scripts/source_inventory.py is available for complex inventories.
Profile key uniqueness and missingness before joining.
Preserve raw fields; create documented normalized fields.
Measure match rates, unmatched records, row multiplication, duplication, and metric conservation for joins.
Validate parsed or transformed values against representative source examples.

For authority matrices and extraction QA, read references/deep-guide.md.