dbt-starrocks-production-readiness

Configure dbt StarRocks production readiness with RBAC, secrets, and slim CI.

14|1|Updated May 5, 2026
One-click install
npx skills add https://github.com/ivanshamaev/de-agent-skills --skill dbt-starrocks-production-readiness
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: dbt-starrocks-production-readiness
Source: https://github.com/ivanshamaev/de-agent-skills/tree/main/group_skills/starrocks_group_skills/dbt_starrocks_production_readiness
Command: npx skills add https://github.com/ivanshamaev/de-agent-skills --skill dbt-starrocks-production-readiness

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you make a dbt project targeting StarRocks safe and reliable in production by covering CI state, permissions, secrets, run/test behavior, and operational practices that prevent silent regressions and breaking changes.

Core Features & Use Cases

  • Slim CI with state:modified+: Builds and tests only changed models and their downstream dependents to reduce costs and feedback time.
  • Production RBAC for dbt and post-hooks: Grants only the minimal StarRocks privileges dbt needs to run, analyze, and access required catalogs/schemas.
  • Secrets management and profiles.yml safety: Uses environment variables (and optional Vault generation) to avoid hardcoded credentials.
  • Breaking change detection: Compares production vs dev relations/columns to fail fast when schema changes would break downstream consumers.
  • Production deployment options and scheduling: Supports running via Airflow or dbt Cloud patterns and includes doc generation/hosting.
  • Operational guardrails: Handles dbt exit codes in CI, ensures manifest.json is uploaded after prod runs, and encourages selective model execution.

Quick Start

Configure your StarRocks RBAC for a dedicated dbt user, store dbt profile credentials via environment variables or Vault, then run CI using state:modified+ against a previously stored production manifest to detect breaking changes before merge.

Frequently Asked Questions about dbt-starrocks-production-readiness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up least-privilege RBAC for dbt targeting StarRocks?

Least-privilege RBAC for dbt on StarRocks requires creating a dedicated dbt user and granting only the minimal privileges needed to run, analyze, and access required catalogs and schemas. This prevents unauthorized access during dbt deployments.

How does slim CI work with dbt state:modified+ for StarRocks?

Slim CI with dbt state:modified+ works by building and testing only changed models and their downstream dependents using a previously stored production manifest. This reduces CI costs and feedback time by skipping unchanged models during StarRocks deployments.

How do I detect breaking schema changes in dbt before merging to StarRocks production?

Breaking change detection in dbt compares production versus development relations and columns to fail fast when schema changes would break downstream consumers. This requires storing the manifest.json artifact and running CI against the previous production state.

What is the best way to manage dbt secrets and profiles.yml for StarRocks connections?

Managing dbt secrets for StarRocks involves using environment variables or optional Vault generation to inject credentials into profiles.yml, avoiding hardcoded credentials and ensuring secure secret injection during production runs.

Can I schedule dbt runs on StarRocks using Airflow or dbt Cloud?

You can schedule dbt runs on StarRocks using either Airflow or dbt Cloud patterns. The setup supports scheduled runs, documentation hosting, and requires uploading the manifest.json artifact after production runs to maintain CI state.

Why do I need to use --defer in dbt development for StarRocks production readiness?

Using --defer in dbt development allows you to compare development objects against the stored production manifest without rebuilding unchanged upstream models. This is necessary for breaking change detection and slim CI to function correctly.