Ejecuta cualquier Skill en Manus
con un clic

Ejecuta cualquier Skill en Manus con un clic

lemma-eda

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Actualizado15 de julio de 2026 a las 00:47

Explore a fresh dataset when the analytical direction is open; use for orientation, pattern discovery, and deciding what analysis is worth pursuing.

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tkpratardan

tkpratardan/lemma

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Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Científicos de datosOcupaciones informáticas y matemáticas·SOC 15-2051

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SKILL.md

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name	lemma-eda
description	Explore a fresh dataset when the analytical direction is open; use for orientation, pattern discovery, and deciding what analysis is worth pursuing.

Exploratory data analysis

Deliver

Provide a trustworthy dataset orientation, a short ranked set of findings, important limitations, and the next analytical question or baseline suggested by the evidence.

Check

Establish observation unit, source identity, coverage, candidate identifiers or targets, and a seed for sampled work.
Inspect shape, types, ranges, duplicates, missingness, time coverage, and group rates. scripts/profile_table.py is available for a deterministic first pass.
Explore only patterns that could change a decision or next step.
Investigate suspicious quality, leakage, subgroup, or temporal patterns before recommending modeling.
State a conclusion for each explored branch.

Do not generate an indiscriminate chart catalog, silently drop missing rows, infer causality, or turn a bounded question into open-ended EDA.

For visualization and chapter patterns, read references/deep-guide.md.

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lemma-leakage

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

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name	lemma-eda
description	Explore a fresh dataset when the analytical direction is open; use for orientation, pattern discovery, and deciding what analysis is worth pursuing.

Exploratory data analysis

Deliver

Provide a trustworthy dataset orientation, a short ranked set of findings, important limitations, and the next analytical question or baseline suggested by the evidence.

Check

Establish observation unit, source identity, coverage, candidate identifiers or targets, and a seed for sampled work.
Inspect shape, types, ranges, duplicates, missingness, time coverage, and group rates. scripts/profile_table.py is available for a deterministic first pass.
Explore only patterns that could change a decision or next step.
Investigate suspicious quality, leakage, subgroup, or temporal patterns before recommending modeling.
State a conclusion for each explored branch.

Do not generate an indiscriminate chart catalog, silently drop missing rows, infer causality, or turn a bounded question into open-ended EDA.

For visualization and chapter patterns, read references/deep-guide.md.