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aidp-profiling-tables

Skill

Profile an AIDP table — row count, per-column null %, distinct count, min/max/mean, and top-K values. Use when the user asks to profile a table, wants column statistics or a data-quality snapshot, or needs to understand a dataset's shape before using it. Runs bounded Spark SQL via the bundled aidp_s

0Machine Learning

Install

git clone https://github.com/oracle-samples/oracle-aidp-samples.git ~/.claude/skills/aidp-profiling-tables

What is aidp-profiling-tables?

Profile an AIDP table — row count, per-column null %, distinct count, min/max/mean, and top-K values. Use when the user asks to profile a table, wants column statistics or a data-quality snapshot, or needs to understand a dataset's shape before using it. Runs bounded Spark SQL via the bundled aidp_sql.py helper.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~78 tokens of context used while enabled, before you invoke anything

Documentation

README · ~1 min read

aidp-profiling-tables — single-table profile

Produce a column-level profile of an AIDP table via Spark SQL. Self-contained: control-plane lookups use oci raw-request; profiling SQL runs through the bundled scripts/aidp_sql.py helper. No aidp MCP server is required.

When to use

  • "Profile ", "what does
    look like", "column stats / data quality snapshot".

    Workflow

    1. Resolve the table (aidp-catalog-explore / .aidp/catalog.md) → fully-qualified catalog.schema.table and its columns/types. Without a cache, list via oci raw-request: GET /tables?catalogKey=<cat>&schemaKey=<cat.schema> and filter for the table client-side (see

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