Aidp Semantic Model

by oracle-samples90b42d6c24d4No licenseListed Oct 8, 2026Updated Oct 8, 2026

Maintain a semantic grounding layer (.aidp/semantic.md) for AIDP — logical entity names, SQL-defined metrics, joins with cardinality, synonyms, and value dictionaries. Use when the user wants to define metrics/business terms, improve NL-to-SQL accuracy, standardize "revenue/customers/etc.", or set up a semantic model. Read by analyzing-data, verified-queries, profiling, and data-quality.

AI-generated overview

Maintains a .aidp/semantic.md business-meaning layer defining metrics, joins, synonyms and value dictionaries for NL-to-SQL grounding.

What it does
Creates and maintains a semantic grounding file, .aidp/semantic.md, that records logical entity names, SQL-defined metrics, joins with cardinality, synonyms and value dictionaries. It follows a documented format and prefers SQL expressions over example SQL and free text. It never invents columns or values, reading them from the catalog or confirming with the user, and can hand off metric validation to a data-analysis skill.
When to use it
Use when the user wants to define metrics or business terms, standardize terms such as revenue or customers, improve natural-language-to-SQL accuracy, or set up a semantic model. It suits teams needing consistent, reusable business semantics across questions.
Requirements
Instructions only; no scripts ship with this skill. It expects an existing .aidp/catalog.md and references external reference documents. Optional metric validation is handed off to another skill that runs Spark SQL via a Python script.

aidp-semantic-model — the semantic grounding layer

Create and maintain .aidp/semantic.md: the business-meaning layer that grounds NL-to-SQL. This is the lever curated systems (Snowflake semantic model, Databricks Genie metrics/joins) rely on — without it, real-world NL-to-SQL accuracy is low.

When to use

  • Define metrics (revenue, active_customers, gross_margin…), logical names, joins, synonyms, or value sets.
  • The user wants consistent, reusable business semantics across questions.

Instruction hierarchy (most → least reliable)

  1. SQL expressions for metrics/filters (preferred).
  2. Example SQL for ambiguous prompts (store these via aidp-verified-queries).
  3. Free text only as a last resort.

Workflow

  1. Ensure .aidp/catalog.md exists (aidp-catalog-init) — the semantic model references real tables/columns.
  2. Edit .aidp/semantic.md per the format in references/semantic-model.md: logical entities, metrics (as SQL expressions), joins (with cardinality), synonyms, value dictionaries.
  3. Never invent columns/values — read them from the catalog or confirm with the user.
  4. Optionally validate a metric by running its SQL on a small sample — hand off to aidp-analyzing-data, which executes Spark SQL via python "$PLUGIN_DIR/scripts/aidp_sql.py" (no MCP required).
  5. Keep the per-domain working set small and focused.

AIDP native Ontologies (related feature — UI-driven)

AIDP ships a native Ontologies feature (RDF/OWL business glossary: terms, synonyms, definitions, a graph view, and ontology-driven governance like av:isSensitive / av:requiresRole). It overlaps this semantic layer but is UI-driven — no programmatic REST API was found (GET …/ontologies and …/workspaces/<ws>/ontologies both returned 404, probed 2026-06-10). So:

  • For an agent-usable, programmatic semantic/glossary layer today, use .aidp/semantic.md (this skill) — it's the API-free analog the agent can read/write and ground SQL with.
  • If the user specifically needs the native Ontologies (graph view, TTL/R2RML export, sensitivity governance), that is authored in the AIDP console UI; don't claim a REST endpoint for it. Sensitivity tags there feed masking governance (aidp-roles-access → masking section).

Notes

  • .aidp/semantic.md is user-editable and git-ignored (per-project).
  • Pairs with aidp-verified-queries (example/verified SQL) and aidp-analyzing-data (consumes both).

References

  • references/semantic-model.md · references/verified-queries.md

Source and attribution

Source:oracle-samples/oracle-aidp-samplesinai/claude-code-plugins/oracle-ai-data-platform-workbench-engineer-agent/skills/aidp-semantic-modelat commit90b42d6

License: No license

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