dbt

dbt data transformation with SQL. Use for data pipelines.

7 stars

byG1Joshi

View on GitHub Installation ↓

Best use case

dbt is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

dbt data transformation with SQL. Use for data pipelines.

Teams using dbt should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/dbt/SKILL.md --create-dirs "https://raw.githubusercontent.com/G1Joshi/Agent-Skills/main/skills/ai-ml/dbt/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/dbt/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How dbt Compares

Feature / Agent	dbt	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

dbt data transformation with SQL. Use for data pipelines.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# dbt (Data Build Tool)

dbt manages data transformation in the warehouse using SQL. v2.0 introduces the **Fusion Engine** (Rust) for performance.

## When to Use

- **Data Modeling**: Converting raw tables into "Gold" tables.
- **Testing**: `not_null`, `unique` tests defined in YAML.
- **Documentation**: Auto-generating data dictionaries.

## Core Concepts

### Models (`.sql`)

Select statements that dbt compiles into `CREATE VIEW/TABLE`.

### Refs (`{{ ref('users') }}`)

Dependency management. dbt builds the DAG automatically.

### Semantic Layer

Defining metrics ("Revenue") in code so all BI tools use the same definition.

## Best Practices (2025)

**Do**:

- **Use Git**: Treat data models like software code.
- **Use Incremental Models**: Only process new data to save cost.
- **Use dbt Mesh**: For cross-project dependencies in large orgs.

**Don't**:

- **Don't put logic in BI tools**: Put it in dbt.

## References

- [dbt Documentation](https://docs.getdbt.com/)