sqlmesh

Plans and applies DAT (actually SQLMesh-supported model changes with automatic backfill awareness.

14|1|Updated May 5, 2026

Search Tags:#ci/cd #data transformation #sqlmesh #audits #scd type 2 #incremental models #dbt migration

Ready to run

npx skills add https://github.com/ivanshamaev/de-agent-skills --skill sqlmesh-ivanshamaev

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sqlmesh
Source: https://github.com/ivanshamaev/de-agent-skills/tree/main/skills/sqlmesh
Command: npx skills add https://github.com/ivanshamaev/de-agent-skills --skill sqlmesh-ivanshamaev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

SQLMesh removes the risk and effort of managing SQL-based data transformations by providing a controlled plan/apply workflow with change classification and state-aware deployments.

Core Features & Use Cases

Model kinds & incremental strategies: Build FULL, VIEW, incremental-by-time-range, incremental-by-unique-key (upserts), and SCD Type 2 models with consistent semantics.
Plan/apply with backfill intelligence: Generate a diff (breaking vs non-breaking vs new) and apply only the required physical table updates and interval backfills.
Audits, unit tests, and CI/CD: Enforce correctness via audits that must return 0 rows and validate model behavior with input/output unit tests and pipelines.
dbt migration support: Import an existing dbt project to accelerate adoption and preserve workflow structure.

Quick Start

Run plan and apply for environment dev to preview changes and then materialize only the required backfills for your SQLMesh models.

Frequently Asked Questions about sqlmesh

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

FAQPage Schema

How do I detect breaking changes in SQL data transformation deployments?▼

Breaking changes in SQL transformations are detected using a plan and apply workflow that generates a diff classifying model updates as breaking, non-breaking, or new to control physical table updates and interval backfills.

What is the best way to build incremental models and SCD Type 2 data transformations?▼

Building incremental models and SCD Type 2 data transformations is best handled by a plan and apply workflow that supports incremental by time range, incremental by unique key, and SCD Type 2 model kinds with state-aware backfill intelligence.

How do I migrate an existing dbt project to SQLMesh?▼

Migrating an existing dbt project to SQLMesh is supported through an import process that accelerates adoption while preserving your existing workflow structure and model semantics for state-backed deployments.

Can I validate SQL model behavior with unit tests and audits before production promotion?▼

Validating SQL model behavior before production promotion is achieved by enforcing correctness through audits that must return 0 rows and validating model behavior with input and output unit tests during CI/CD pipelines.

Why do I need state-aware deployments for SQL transformations?▼

State-aware deployments for SQL transformations are needed to remove the risk of applying changes by tracking previous deploy states, ensuring that only the required physical table updates and interval backfills are materialized across development and production.