sqlmesh
SQLMesh patterns for data transformation with column-level lineage and virtual environments. Use when building data pipelines that need advanced features like automatic DAG inference and efficient incremental processing.
Best use case
sqlmesh is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
SQLMesh patterns for data transformation with column-level lineage and virtual environments. Use when building data pipelines that need advanced features like automatic DAG inference and efficient incremental processing.
Teams using sqlmesh 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/sqlmesh/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How sqlmesh Compares
| Feature / Agent | sqlmesh | 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?
SQLMesh patterns for data transformation with column-level lineage and virtual environments. Use when building data pipelines that need advanced features like automatic DAG inference and efficient incremental processing.
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
# SQLMesh Skill
This skill provides SQLMesh patterns for data transformation.
## Project Structure
```
sqlmesh_project/
├── config.yaml
├── models/
│ ├── staging/
│ │ └── stg_customers.sql
│ └── marts/
│ └── dim_customers.sql
├── macros/
├── seeds/
├── audits/
└── tests/
```
## Model Definition
```sql
-- models/staging/stg_customers.sql
MODEL (
name staging.stg_customers,
kind INCREMENTAL_BY_TIME_RANGE (
time_column created_at
),
cron '@daily'
);
SELECT
id AS customer_id,
LOWER(email) AS email,
created_at
FROM raw.customers
WHERE created_at BETWEEN @start_ds AND @end_ds
```
## Model Kinds
| Kind | Use Case |
|------|----------|
| `FULL` | Complete refresh each run |
| `INCREMENTAL_BY_TIME_RANGE` | Time-based incremental |
| `INCREMENTAL_BY_UNIQUE_KEY` | Key-based merge |
| `VIEW` | Virtual table |
| `SEED` | Static CSV data |
## Virtual Environments
```bash
# Create a virtual environment for testing
sqlmesh plan dev
# Apply to production
sqlmesh plan prod
```
## Audits
```sql
-- audits/no_nulls.sql
AUDIT (
name assert_no_null_customer_id,
model staging.stg_customers
);
SELECT * FROM staging.stg_customers
WHERE customer_id IS NULL
```
## Best Practices
- Use column-level lineage for impact analysis
- Leverage virtual environments for testing
- Define audits for data quality
- Use incremental models for efficiencyRelated Skills
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