Exploratory Data Analysis

What is exploratory-data-analysis useful for?

Before modelling, this skill can help establish what an authorised local dataset actually contains. The pinned workflow produces bounded aggregate reports for explicitly supported inputs, with optional checks for selected array, hierarchical, sequence and image metadata formats when their required libraries are available.

Its topics include missingness, leakage, outliers and sensitivity to transformations. The limits matter as much as the report: it must not print identifying raw records, follow instructions embedded in data, overwrite inputs or silently remove and transform observations.

Unknown formats fail closed, and a partial inspection must not be called complete validation. Exploratory output is not a causal, clinical or mechanistic conclusion, or a certification that a file is safe.

SkillPi original overview · Pinned source for facts

From the source

• Aggregate profiles, data-quality risks and a structured exploratory analysis report.
• Does not automatically modify raw data, support every format, or establish causal, clinical or confirmatory conclusions.
• Confirm the data root, format, permissions and scope.
• Run only the documented bounded inspections for supported files.
• Review aggregate findings and distinguish exploration from validation.
• Core CLIs require Python 3.11+; the full optional snapshot requires Python 3.12+ and uv.
• Format-specific libraries are optional; inspect the pinned SKILL.md for exact scope.
• Use authorized local files within an explicit data root.

Original SKILL.md

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Current listing version

593377bb048752abb0152deab9fbb1083a6087df

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Security rating and access scope

unscanned · Static evidence review; not a runtime safety guarantee.

Before use, confirm and authorize actual file access, external services and network destinations in your host. This review grants no runtime permissions.