Exploratory Data Analysis for tabular data. Use this skill when analyzing value distributions, checking for missing data, computing correlations, examining class balance, or generating data quality reports.
Analyzes tabular data distributions, missing values, and correlations to understand dataset quality before modeling. Triggers when you need to examine value distributions, check for missing data, compute correlations, or generate data quality reports.
/plugin marketplace add argythana/python-ml-skills/plugin install argythana-python-ml-skills@argythana/python-ml-skillsThis skill inherits all available tools. When active, it can use any tool Claude has access to.
pyproject.tomlsrc/skill_eda/__init__.pysrc/skill_eda/column_dist.pytasks/column_distribution.mdAnalyze tabular datasets to understand distributions, data quality, and relationships between variables.
| Task | Command | Description |
|---|---|---|
| Column Distribution | eda-column-dist | Analyze value distribution for a specific column |
Detailed task templates are available in tasks/:
tasks/column_distribution.md - Full documentation for column distribution analysis# Analyze distribution of a column
eda-column-dist --source <path> --column <name>
# Save report to file
eda-column-dist --source <path> --column <name> --output report.md
All EDA scripts produce markdown reports with:
Use when working with Payload CMS projects (payload.config.ts, collections, fields, hooks, access control, Payload API). Use when debugging validation errors, security issues, relationship queries, transactions, or hook behavior.