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aidp-ai-sql

Skill

Run LLM functions inside Spark SQL on AIDP via ai_generate(). Use when the user wants to summarize/classify/extract/enrich rows with an LLM directly in SQL, generate narratives over aggregated results, or do grounded RAG-style analysis in the lakehouse. Signature is model-first; available models mus

0Machine Learning

Install

git clone https://github.com/oracle-samples/oracle-aidp-samples.git ~/.claude/skills/aidp-ai-sql

What is aidp-ai-sql?

Run LLM functions inside Spark SQL on AIDP via ai_generate(). Use when the user wants to summarize/classify/extract/enrich rows with an LLM directly in SQL, generate narratives over aggregated results, or do grounded RAG-style analysis in the lakehouse. Signature is model-first; available models must be confirmed live before relying on it.

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

~85 tokens of context used while enabled, before you invoke anything

Documentation

README · ~2 min read

aidp-ai-sql — LLM-in-SQL with ai_generate()

Call an LLM directly inside Spark SQL on AIDP — summarize, classify, extract, or narrate over lakehouse data without leaving SQL. A signature differentiator: most competitor agents can't do this inline.

This is a SQL-helper skill. Interactive Spark SQL runs through the bundled helper scripts/aidp_sql.py (it mints a UPST from the api_key DEFAULT profile, auto-creates a scratch notebook, and returns JSON). No aidp MCP and no AIDP_SESSION required.

When to use

  • "Summarize / classify / extract / enrich these rows with AI in SQL."
  • Generate a grounded narrative over an aggregate (e.g. a finance summary over a spend rollup).

Signature (model FIRST)

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