dbt-model-generator
Generate dbt model generator operations. Auto-activating skill for Data Pipelines. Triggers on: dbt model generator, dbt model generator Part of the Data Pipelines skill category. Use when working with dbt model generator functionality. Trigger with phrases like "dbt model generator", "dbt generator", "dbt". '
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill dbt-model-generator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Dbt Model Generator ## Overview This skill provides automated assistance for dbt model generator tasks within the Data Pipelines domain. ## When to Use This skill activates automatically when you: - Mention "dbt model generator" in your request - Ask about dbt model generator patterns or best practices - Need help with data pipeline skills covering etl, data transformation, workflow orchestratio
What does the dbt-model-generator skill do?
Generate dbt model generator operations. Auto-activating skill for Data Pipelines. Triggers on: dbt model generator, dbt model generator Part of the Data Pipelines skill category. Use when working with dbt model generator functionality. Trigger with phrases like "dbt model generator", "dbt generator", "dbt". '
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill dbt-model-generator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jeremylongshore/claude-code-plugins-plus-skills, a repository with 2,596 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.