Execute qualquer Skill no Manus
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Execute qualquer Skill no Manus com um clique

eda

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Atualizado5 de maio de 2026 às 11:46

Use this skill when the user wants exploratory analysis of one tabular dataset (Parquet, CSV, JSON, JSONL) — column distributions, data quality, class balance, missing patterns, high-cardinality detection, or summary statistics. Trigger on prompts like "analyze this column", "check the distribution", "look for missing values", "is this target imbalanced", or "run EDA on this file". If the dataset path or column is missing, ask for it after routing. Do NOT use it just to verify a file is readable, list its schema, or count rows; use `dataset-inspector` first for basic access checks. Do NOT use it for model training, feature engineering, non-tabular data (images, text, audio), or remote warehouse exploration.

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

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Fonte

argythana

argythana/python-ml-skills

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Cientistas de dadosInformática e Matemática·SOC 15-2051

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3 arquivos

SKILL.md

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name

eda

description

Exploratory Data Analysis

Analyze tabular datasets to understand distributions, data quality, and patterns.

When To Use

Understanding a new dataset before modeling
Checking data quality such as missing values, outliers, and duplicates
Analyzing target variable distribution
Identifying class imbalance
Generating summary statistics

Analysis Process

Connect to data and inspect schema.
Analyze the target variable first.
Check each relevant column for distribution, missingness, and cardinality.
Document findings in a compact report.

Helper

For local Parquet, CSV, JSON, or JSONL files, use:

scripts/eda-column-dist --source data/sample.parquet --column status

The helper script requires duckdb in the active Python environment.

Reference

For detailed analysis methodology and output format, read references/eda-analysis.md.

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name

eda

description

Exploratory Data Analysis

Analyze tabular datasets to understand distributions, data quality, and patterns.

When To Use

Understanding a new dataset before modeling
Checking data quality such as missing values, outliers, and duplicates
Analyzing target variable distribution
Identifying class imbalance
Generating summary statistics

Analysis Process

Connect to data and inspect schema.
Analyze the target variable first.
Check each relevant column for distribution, missingness, and cardinality.
Document findings in a compact report.

Helper

For local Parquet, CSV, JSON, or JSONL files, use:

scripts/eda-column-dist --source data/sample.parquet --column status

The helper script requires duckdb in the active Python environment.

Reference

For detailed analysis methodology and output format, read references/eda-analysis.md.