ClaudeSuperPower

aidp-data-quality

Skill

Run data-quality rule checks on AIDP tables — not-null, uniqueness, allowed ranges/sets, referential integrity, and freshness. Use when the user wants to validate data, check for nulls/duplicates/orphans, assert a column's domain, or gate a pipeline on quality. Expresses each rule as bounded Spark S

Install

git clone https://github.com/oracle-samples/oracle-aidp-samples.git ~/.claude/skills/aidp-data-quality

What is aidp-data-quality?

Run data-quality rule checks on AIDP tables — not-null, uniqueness, allowed ranges/sets, referential integrity, and freshness. Use when the user wants to validate data, check for nulls/duplicates/orphans, assert a column's domain, or gate a pipeline on quality. Expresses each rule as bounded Spark SQL and reports pass/fail with offending counts.

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

~87 tokens of context used while enabled, before you invoke anything

Documentation

README · ~2 min read

aidp-data-quality — rule checks via Spark SQL

Validate AIDP tables against explicit data-quality rules, each compiled to bounded Spark SQL and executed with the bundled helper — no MCP and no ai-data-engineer-agent repo required.

When to use

  • "Check for nulls/duplicates", "validate ", "are there orphan rows", "is the data fresh", or gating a pipeline on quality.

    Rule types (each → a counting SQL that should return 0 violations)

    RuleCheck (violations)
    not-nullCOUNT(*) WHERE col IS NULL

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