data-migration-expert

Name: data-migration-expert
Availability: InStock
Author: andreicr1

Validate data migration mappings and rollback safety for PRs.

Updated Mar 14, 2026

Search Tags:#verification #data-integrity #backfill #schema-migration #data-migration #production-validation

Ready to run

npx skills add https://github.com/Andreicr1/netz-analysis-engine --skill data-migration-expert-andreicr1

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-migration-expert
Source: https://github.com/Andreicr1/netz-analysis-engine/tree/main/.gemini/skills/data-migration-expert
Command: npx skills add https://github.com/Andreicr1/netz-analysis-engine --skill data-migration-expert-andreicr1

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Validates that data migrations, backfills, and production data transformations align with reality, preventing data corruption by verifying mappings, catching swapped values, and ensuring testing coverage before deploy.

Core Features & Use Cases

Verify mappings match production data and do not rely on fixtures or assumptions
Detect swapped or inverted values to prevent data corruption
Provide concrete verification plans with SQL queries to prove correctness post-deploy
Validate rollback safety through feature flags, dual-writes, and staged deployments

Quick Start

Run a live production data snapshot against the migration plan to generate verification queries and rollback checks.

Frequently Asked Questions about data-migration-expert

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

FAQPage Schema

How do I validate data migration mappings against production data?▼

To validate data migration mappings, run a live production data snapshot against the migration plan to generate verification queries and rollback checks. This proves mapping correctness by relying on actual production data rather than fixtures or assumptions.

What is the best way to detect swapped or inverted values during a schema migration?▼

Detecting swapped or inverted values during a schema migration requires validating mappings against production reality. This approach catches data corruption early by verifying that ID mappings, enum conversions, and column renames align with actual data.

How do I create a concrete verification plan for a database backfill?▼

Creating a concrete verification plan for a database backfill involves generating SQL queries to prove correctness post-deploy. The plan validates that backfills align with reality and prevents data corruption by enforcing testing coverage before deployment.

How do I validate rollback safety for production data transformations?▼

Validating rollback safety for production data transformations involves enforcing feature flags, dual-writes, and staged deployments. These mechanisms ensure you can safely revert schema changes or enum conversions if data corruption is detected post-deploy.

Can I use production data validation for PRs involving column renames and enum conversions?▼

Yes, production data validation is designed to be applied to PRs involving ID mappings, column renames, enum conversions, and schema changes. It ensures mappings do not rely on assumptions and enforces observability after deploy.

Why does my data migration fail to catch data corruption before deploy?▼

Data migration fails to catch corruption before deploy when verification relies on fixtures or assumptions rather than production reality. Validating mappings against live production data and enforcing concrete verification plans prevents this issue.