Interface for defining, testing, and applying data models and pipelines using SQLMesh.
Install
mkdir -p .claude/skills/sqlmesh && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/9544" && unzip -o skill.zip -d .claude/skills/sqlmesh && rm skill.zipInstalls to .claude/skills/sqlmesh
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Use when working with SQLMesh — writing or editing MODEL blocks, Python @model decorators, Python @macros, audits, unit tests, external_models.yaml, or seeds; running `sqlmesh plan/apply/audit/render/evaluate/test`; debugging plans, snapshots, virtual environments, or state issues; configuring `config.py`, gateways, or `before_all`/`after_all` hooks; choosing a model kind (FULL, INCREMENTAL_BY_TIME_RANGE, INCREMENTAL_BY_UNIQUE_KEY, VIEW, SEED, EMBEDDED, SCD_TYPE_2, EXTERNAL, MANAGED); migrating a project from dbt; or asking whether SQLMesh is still maintained. Trigger on these terms even when the user does not name the tool. SQLMesh changes quickly and the model's prior knowledge is often wrong — fetch the canonical docs at https://sqlmesh.readthedocs.io/en/stable/ before answering anything non-trivial.Key capabilities
- →Write models and macros
- →Run sqlmesh plans
- →Execute audits
- →Test data pipelines
How it works
Uses SQLMesh to manage data transformations, snapshots, and virtual environments.
Inputs & outputs
When to use sqlmesh
- →Defining data models
- →Running sqlmesh plans
- →Testing data pipeline logic
- →Migrating from dbt
About sqlmesh
Handles data transformation tasks in SQLMesh. Supports writing models, macros, and audits; running plans and tests; and configuring project environments.
Use when working with SQLMesh — writing or editing MODEL blocks, Python @model decorators, Python @macros, audits, unit tests, external_models.yaml, or seeds; running `sqlmesh plan/apply/audit/render/evaluate/test`; debugging plans, snapshots, virtual environments, or state issues; configuring `conf
When not to use it
- →Using Tobiko Cloud features
- →Directly porting dbt incremental models
Prerequisites
Limitations
- →State backend requires Postgres for concurrency
- →Jinja macros have known footguns
How it compares
Uses a snapshot-based virtual environment approach rather than dbt's direct materialization.
Compared to similar skills
sqlmesh side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| sqlmesh (this skill) | 0 | 4mo | Review | Advanced |
| data-quality-frameworks | 0 | 5mo | No flags | Intermediate |
| soda-core | 0 | 4mo | Review | Intermediate |
| data-dbt-guide | 0 | 3mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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