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aidp-analyzing-data

aidp-analyzing-data is a skill for Claude Code, Codex from ahmedawan-oracle/claude-code-plugins. It costs 100 tokens per session (1,071 once invoked), scanned A, original, MIT.

A way to answer business questions using Spark SQL, a language for querying data with the Spark analytics engine, over the AIDP lakehouse.

In plain words
What is it for?
Use it for counts, rankings, trends, revenue breakdowns, and other ad-hoc questions about data already stored in the AIDP lakehouse.
Why use it?
It connects everyday questions to the correct lakehouse tables and definitions before running a query. This helps avoid guessed table joins, misunderstood business terms, and unsupported results.

Skill for Claude CodeCodex

Part of the oracle-ai-data-platform-workbench-engineer-agent plugin — 14 skills shipped together

not rated 2 1mo ago A scan 100 tokens original MIT
View the source file ahmedawan-oracle/claude-code-plugins
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/ahmedawan-oracle/claude-code-plugins/aidp-analyzing-data
Any agent
npx skills add ahmedawan-oracle/claude-code-plugins --skill aidp-analyzing-data
Clone the repo
git clone --depth 1 https://github.com/ahmedawan-oracle/claude-code-plugins

Made for: Claude Code, Codex.

Or install oracle-ai-data-platform-workbench-engineer-agent, the plugin that ships this one along with the rest of its 14 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for aidp-analyzing-data

README.md
[![agentmods](https://agentmods.dev/badge/skills/ahmedawan-oracle/claude-code-plugins/aidp-analyzing-data.svg)](https://agentmods.dev/skills/ahmedawan-oracle/claude-code-plugins/aidp-analyzing-data)
Your own site
<a href="https://agentmods.dev/skills/ahmedawan-oracle/claude-code-plugins/aidp-analyzing-data"><img src="https://agentmods.dev/badge/skills/ahmedawan-oracle/claude-code-plugins/aidp-analyzing-data.svg" alt="Measured on agentmods" height="20"></a>
Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,071 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00100 $0.01071
Opus 5 $0.00050 $0.00535
Sonnet 5 $0.00020 $0.00214
Haiku 4.5 $0.00010 $0.00107

Measured 4d ago against content hash 00057957cb55, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

aidp-analyzing-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

claude-code-plugins/oracle-ai-data-platform-workbench-engineer-agent/skills/aidp-analyzing-data/SKILL.md · 58 lines

How it starts

The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Workflow (grounding-first — this is the accuracy lever)

  1. Verified-query match. Read .aidp/verified-queries.md; if a verified: true entry closely matches the question (similar text + table overlap), reuse its SQL (adapt only dates/bind values) and say so.
  2. Ground. Otherwise read .aidp/catalog.md + .aidp/semantic.md: map concepts→tables via Quick Reference/synonyms, use recorded join keys (don't guess joins), use value dictionaries for WHERE literals, prefer metric SQL expressions from the semantic model. If the catalog cache is missing, run aidp-catalog-init first.
  3. Scope small. Use the few tables the question needs; for big fact tables add date filters; consider a pre-joined view for repeated complex asks.
  4. Execute. Run the SQL via the bundled helper — it mints a UPST from the api_key DEFAULT profile and auto-creates a scratch notebook on the target cluster (no MCP, no AIDP_SESSION required): 
    python "$PLUGIN_DIR/scripts/aidp_sql.py" \
  --region <region> --datalake <DATALAKE_OCID> --workspace <ws> --cluster <cluster-key> \
  --code "spark.sql('''<SQL>''').show(50, truncate=False)"
    Returns JSON {status, execution_count, outputs, spark_job_ids, error}. Each invocation runs the cell; keep the same <SQL> shape across follow-ups. Smoke-test connectivity with --code "spark.sql('SELECT 1').show()".
  5. Present the result clearly (table + a one-line read of what it shows). Show the SQL you ran.
  6. Cache the learning. Offer to save a new concept→table mapping to .aidp/catalog.md and/or register the working query via aidp-verified-queries (which validates before marking it verified).

Read the full file on GitHub · 58 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 58 lines · 100 tokens per session scan A 00057957cb55

Subscribe to this mod's changes

aidp-analyzing-data is a skill published in the GitHub repository ahmedawan-oracle/claude-code-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 100 tokens to every session and 1,071 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Analytics and metricsDatabases
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