from kilo-marketplace84
Guides agents to build, modify, test, and validate dbt models and SQL transformations for analytics engineering workflows.
This skill equips an agent with best-practice guidance for working with dbt: planning and building models, using ref() and source(), writing tests, debugging errors, and validating results via dbt show. It emphasizes software-engineering principles (DRY, modularity, testing) applied to data transformations and includes reference guides for planning models, discovering data, writing tests, and debugging.
Use this skill when constructing new dbt models, refactoring or extending existing models, creating analytics pipelines, writing data tests, or investigating failures in dbt runs. Avoid using it for semantic-layer question answering; use dedicated skills for that.
scripts/ present)references/ (planning, discovering data, tests, debugging){{ ref }} and {{ source }}, running dbt show for validation, cost-management tips, and CLI interaction patterns.Likely compatible with agents that can run shell commands and interact with CLIs (Copilot/Codex-like agents, Kilo CLI-enabled agents).
Well-structured dbt analytics engineering skill with clear triggers, progressive disclosure via references/, and practical DAG/model building guidelines. The bundled script is a markdown-embedded Python snippet for reviewing dbt run results that couldn't execute standalone. SKILL.md includes security-conscious advice about treating external data as untrusted.
Clean skill with no security concerns. Script is instructional Python within markdown rather than a standalone file, which is why run_scripts.py skipped it. The skill itself is well-organized with good reference guide structure.
