---
name: "mage-ai-data-pipeline-integration"
description: "Build and run data pipelines with hybrid UI/code notebooks and instant data previewing."
metadata:
  author: "Mage"
  version: "1.0.0"
  runtime_compatibility:
    - "codex"
    - "claude-code"
    - "cursor"
    - "windsurf"
triggers:
  - "build mage data loader"
  - "write mage transformer block"
  - "test block assertions mage"
permissions:
  fs_read: true
  fs_write: "workspace"
  shell_exec: false
  net_access: false
---

# Mage.ai Modern Data Pipeline Engineering

Develops ETL pipelines using Mage.ai: writing data loaders, transformers, and data exporters, testing data quality with block-level assertions, setting up triggers, and deploying on Kubernetes.

## When to Invoke
Invoke this skill whenever you need to:
- build mage data loader
- write mage transformer block
- test block assertions mage

## Execution SOP & Rules
1. Create blocks using standard Mage decorators: @data_loader, @transformer, and @data_exporter.
2. Add assert statements inside block test functions to automatically halt pipeline on schema anomalies.
3. Configure pipeline triggers (schedule or event-based) in the Mage UI or pipelines.yaml.
4. Export pipeline definitions and manage version control cleanly via standard Git workflows.

## Verification Checklist
- Run local linting, syntax validation, and type-checks before reporting completion.
- Ensure all created or modified files adhere to workspace boundaries and avoid uncommitted secrets.
- Verify tests or build commands exit cleanly with return code 0.