What frameworks does this migration skill support?

It provides standardized patterns and examples for major frameworks including Python's Alembic, Ruby on Rails' ActiveRecord, and JavaScript's Sequelize.

Why are schema and data migrations separated?

Separating them ensures structural changes remain fast and predictable, while slower data transformations can be managed and rolled back independently without affecting the schema version.

Can it help with large-scale data updates?

Yes, the skill enforces batch processing and idempotency in data migrations to prevent memory issues and ensure scripts can be safely re-run if interrupted.

How does it handle zero-downtime deployments?

The skill guides a multi-step approach for breaking changes (like renaming columns) and uses concurrent indexing techniques to prevent table locks during schema updates.

Backend Migration Standards

Name: Backend Migration Standards
Author: Microck

byMicrock

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Database Management

Enforces professional database migration practices including reversibility, zero-downtime strategies, and standardized naming conventions.

About

This skill provides Claude Code with a comprehensive framework for managing database evolution with extreme care and precision. It focuses on creating safe, reversible schema changes and data migrations that prevent service interruptions. By implementing best practices like concurrent index creation, idempotent data scripts, and multi-step column modifications, this skill ensures that database updates are production-ready across various environments and frameworks such as Alembic, Rails, and Sequelize.

Key Features

Concurrent index management for high-traffic production tables
Separation of structural schema changes from operational data migrations
Automated adherence to timestamped and descriptive naming conventions
81 GitHub stars
Mandatory reversibility for all schema changes with tested rollback methods
Zero-downtime deployment strategies for non-blocking database modifications

Use Cases

Batch processing massive data migrations with idempotent scripts
Renaming or removing columns using safe, multi-step deployment patterns
Adding columns to large production tables without locking the database

About

Key Features

Concurrent index management for high-traffic production tables
Separation of structural schema changes from operational data migrations
Automated adherence to timestamped and descriptive naming conventions
81 GitHub stars
Mandatory reversibility for all schema changes with tested rollback methods
Zero-downtime deployment strategies for non-blocking database modifications

Use Cases

Batch processing massive data migrations with idempotent scripts
Renaming or removing columns using safe, multi-step deployment patterns
Adding columns to large production tables without locking the database