dbt-to-smml
Skill14 starsChanged 38 days agoLicence not identifiedView on GitHub
Generate an Oracle Analytics Cloud (OAC) semantic model in SMML (Semantic Modeler Markup Language) from a dbt project. Use when the user wants to build, convert, or scaffold an OAC semantic model from dbt models — generate SMML, the physical/logical/presentation layers, measures, hierarchies and role-playing dimensions — driven by dbt metadata plus `meta.oac` annotations in schema.yml. Triggers: "generate SMML", "dbt to OAC semantic model", "build the OAC semantic layer from dbt", "create logical/physical/presentation layer from dbt". For hand-authoring, editing, or reviewing SMML directly (no dbt project), use the sibling `smml-semantic-modeling` skill instead — this skill's `meta.oac` vocabulary and generator are built on that skill's modeling knowledge.
In this repository74 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- add-analytics-instrumentationSkill
- agents-schemaSkill
- airbyteSkill
- airflowSkill
- analyze-account-healthSkill
- analyze-ai-topicsSkill
- analyze-chartSkill
- analyze-dashboardSkill
- analyze-experimentsSkill
- analyze-feedbackSkill
- bigquery-basicsSkill
- cloud-run-basicsSkill
- compare-user-journeysSkill
- coupler-ioSkill
- create-chartSkill
- create-dashboardSkill
- cubeSkill
- dagsterSkill
- daily-briefSkill
- data-appsSkill
- dbt-analytics-qaSkill
- dbtchartsSkill
- dbt-dagSkill
- dbt-developmentSkill
- dbt-fusionSkill
- dbt-mcp-serverSkill
- dbt-migrationSkill
- dbt-semantic-layerSkill
- dbt-troubleshootingSkill
- dbt-unit-testingSkill
- debug-replaySkill
- diagnose-errorsSkill
- diff-intakeSkill
- dignified-pythonSkill
- discover-analytics-patternsSkill
- discover-event-surfacesSkill
- discover-opportunitiesSkill
- droughtySkill
- Engagement ContextSkill
- engagement-status-reportSkill
- Fathom SyncSkill
- fivetranSkill
- gcloudSkill
- google-cloud-recipe-authSkill
- google-cloud-waf-cost-optimizationSkill
- google-cloud-waf-securitySkill
- hightouchSkill
- instrument-eventsSkill
- investigate-ai-sessionSkill
- looker-dashboard-mockupSkill
- lookml-content-authoringSkill
- lookml-content-authoringSkill
- metabaseSkill
- monitor-ai-qualitySkill
- monitor-experimentsSkill
- monitor-reliabilitySkill
- omniSkill
- project-reviewSkill
- Release DirectorSkill
- The first 60 shown.
The file
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

