Agent-Skills.md

Agent Skills: Exploratory Data Analysis

Exploratory Data Analysis for tabular data. Use when analyzing column distributions, checking data quality, examining class balance, detecting missing patterns, or generating summary statistics for datasets.

UncategorizedID: argythana/python-ml-skills/eda

Repository

argythana/python-ml-skills
argythana

Install this agent skill to your local

pnpm dlx add-skill https://github.com/argythana/python-ml-skills/tree/HEAD/eda

Skill Files

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eda/SKILL.md

Skill Metadata

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

  1. Connect to data and inspect schema.
  2. Analyze the target variable first.
  3. Check each relevant column for distribution, missingness, and cardinality.
  4. 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.