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molt-verify

Compare source and target databases to validate schema and data integrity after migration.

3|3|Updated Mar 11, 2026
Search Tags:#database#schema#migration#data-integrity#verify#molt
One-click install
npx skills add https://github.com/cockroachdb/claude-plugin --skill molt-verify-cockroachdb
Or copy as Structured Prompt for Agentโ–ผ
Please help me install this Agent Skill.
Skill: molt-verify
Source: https://github.com/cockroachdb/claude-plugin/tree/main/skills/onboarding-and-migrations/molt-verify
Command: npx skills add https://github.com/cockroachdb/claude-plugin --skill molt-verify-cockroachdb

SYSTEM DOCUMENTATION & REQUIREMENTS

๐Ÿ’ก This Skill includes references (resource) components.

What problem does it solve?

After migrating data between source and target databases, ensure schema and data consistency to prevent post-migration issues.

Core Features & Use Cases

  • Schema comparison to detect missing tables, column types, NOT NULL constraints, and PK differences.
  • Row-level data verification with configurable concurrency and shards to validate data integrity post-migration.
  • Modes and options (full verification, schema-only, compile-only) and guidance for safe operation in production environments.

Quick Start

Run molt verify with --source and --target connection strings to perform a full migration integrity check.

Frequently Asked Questions about molt-verify

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

FAQPage Schema
How do I verify data integrity after a database migration?โ–ผ

Verify data integrity after database migration by comparing source and target databases to validate schema and row-level consistency. Use the molt verify command with source and target connection strings to perform a full integrity check.

Can I check for schema differences like missing tables or constraint changes between databases?โ–ผ

Schema comparison detects missing tables, column types, NOT NULL constraints, and primary key differences between databases. This schema verification ensures structural consistency post-migration.

How do I run row-level data verification in production environments?โ–ผ

Row-level data verification supports configurable concurrency and sharding to validate data integrity in production. Modes like full verification, schema-only, and compile-only offer guidance for safe operation under production workloads.

What do I need to perform a migration integrity check across databases?โ–ผ

Performing a migration integrity check requires the molt binary and proper source and target database connection strings. The tool validates these inputs before reporting schema and row verification results.

Does database verification work with sharding for large datasets?โ–ผ

Database verification works with sharding and configurable concurrency to handle large datasets. These options distribute the row-level validation workload, ensuring data integrity checks scale for production environments.

What are the limitations of running schema-only verification?โ–ผ

Schema-only verification limits checks to structural elements like missing tables, column types, and constraints, skipping row-level data validation. Use full verification mode instead to validate both schema and data integrity comprehensively.

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