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aidp-analyzing-data

Skill

Answer business questions over the AIDP lakehouse with Spark SQL. Use when the user asks a data question ("how many…", "top N…", "show me…", "trend of…", "revenue by…") or wants to run ad-hoc Spark SQL. Grounds in .aidp/catalog.md + .aidp/semantic.md and reuses validated verified queries before gene

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Install

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

What is aidp-analyzing-data?

Answer business questions over the AIDP lakehouse with Spark SQL. Use when the user asks a data question ("how many…", "top N…", "show me…", "trend of…", "revenue by…") or wants to run ad-hoc Spark SQL. Grounds in .aidp/catalog.md + .aidp/semantic.md and reuses validated verified queries before generating SQL, then executes 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

~90 tokens of context used while enabled, before you invoke anything

Documentation

README · ~2 min read

aidp-analyzing-data — natural language → Spark SQL

Answer business questions by grounding in the catalog/semantic model, reusing verified queries when possible, then executing Spark SQL via the bundled scripts/aidp_sql.py helper.

When to use

  • Any data question, or "run this SQL on AIDP".

Source is an external / non-lakehouse system (Fusion, EPM, Oracle ADB/ExaCS, Snowflake, S3, …)? This skill is lakehouse-native Spark SQL. To pull from an external source, use the oracle-ai-data-platform-workbench-spark-connectors plugin's aidp-<source> skill (install it if absent; run its aidp-connectors-bootstrap skill once to push the helper package to the cluster), or aidp-federate to join across sources.

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