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aidp-semantic-model

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.

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oracle-samples/oracle-aidp-samples
Dernière activité de la source
24 juin 2026 à 07:21
Langue détectée de SKILL.md
anglais
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47
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32

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SKILL.md
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name
aidp-semantic-model
description
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.
# `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 "$HOME/.aidp/aidp_sql.py"` (no MCP required). 5. Keep the per-domain working set small and focused. ## AIDP native Ontologies (related feature — UI-driven) AIDP ships a native **Ontologies** feature (RDF/OWL business glossary: terms, synonyms, definitions, a graph view, and ontology-driven governance like `av:isSensitive` / `av:requiresRole`). It overlaps this semantic layer but is **UI-driven** — **no programmatic REST API was found** (`GET …/ontologies` and `…/workspaces/<ws>/ontologies` both returned **404**, probed 2026-06-10). So: - For an **agent-usable, programmatic** semantic/glossary layer today, use `.aidp/semantic.md` (this skill) — it's the API-free analog the agent can read/write and ground SQL with. - If the user specifically needs the **native Ontologies** (graph view, TTL/R2RML export, sensitivity governance), that is authored in the AIDP console UI; don't claim a REST endpoint for it. Sensitivity tags there feed masking governance (`aidp-roles-access` → masking section). ## Notes - `.aidp/semantic.md` is user-editable and git-ignored (per-project). - Pairs with `aidp-verified-queries` (example/verified SQL) and `aidp-analyzing-data` (consumes both). ## References - [references/semantic-model.md]($HOME/.aidp/references/semantic-model.md) · [references/verified-queries.md]($HOME/.aidp/references/verified-queries.md)
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