smithery.ai

data-consistency-review

Data consistency review guidance. Use when reviewing database changes, migrations, schema modifications, or data integrity concerns.

First seen Mar 20, 2026

Installation

$ npx skills add https://smithery.ai

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from smithery.ai · top by installs.

npx skills add https://smithery.ai

Browse all from smithery.ai

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,140 B
  • docs SUMMARY.md 163 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

Report ONLY when you found something concerning and then report what you found. If you didn't find anything, don't report it. Mention only things you actually found. Don't "check off" certain points here by saying "passed" this or that point. I assume when you didn't mention an aspect that is precisely because it PASSED. (But, if there is really absolutely nothing you found, at least acknowledge with a single statement that you did found nothing.)

Data Consistency Review

Exports

  • User self serviced Backups (export functionality) breaks older backups?

DB

Focus Areas

  • Migration safety and reversibility
  • Schema soundness and normalization
  • Data integrity constraints
  • Backward compatibility with existing data
  • Production data impact assessment
  • Transaction boundaries and atomicity

Process

  1. Identify all schema changes and migrations
  2. Assess impact on existing production data
  3. Verify migrations are idempotent or safely repeatable
  4. Check for potential data loss scenarios
  5. Review rollback strategy
  6. Validate constraint additions against existing data

Migration Safety

Before Deploying

  • Can the migration run on production data without failing?
  • Are there NULL values that would violate new NOT NULL constraints?
  • Are there duplicate values that would violate new UNIQUE constraints?
  • Will foreign key additions find orphaned records?
  • Is the migration small enough to complete without locking issues?

Destructive Changes

Flag these as high-risk:

  • Dropping columns or tables
  • Changing column types (especially narrowing)
  • Adding NOT NULL without defaults
  • Removing or modifying constraints
  • Renaming columns/tables (breaks existing queries)

Safe Patterns

  • Add columns as nullable first, backfill, then add constraint
  • Create new table, migrate data, swap references, drop old
  • Use feature flags to decouple deploy from migration

Schema Soundness

Normalization

  • Avoid redundant data that can become inconsistent
  • Use foreign keys to enforce relationships
  • Consider denormalization only with clear justification

Constraints

  • Primary keys on all tables
  • Foreign keys for relationships
  • NOT NULL where business logic requires values
  • CHECK constraints for domain validation
  • UNIQUE constraints for natural keys

Indexing

  • Indexes on foreign keys
  • Indexes on frequently queried columns
  • Composite indexes match query patterns
  • Avoid over-indexing (write performance)

Production Data Concerns

Data Volume

  • Will queries still perform with 10x/100x data?
  • Are there full table scans in migrations?
  • Index creation on large tables may lock

Edge Cases

  • Empty strings vs NULL handling
  • Zero values vs NULL for numbers
  • Timezone handling for dates
  • Unicode and special characters

Rollback Strategy

  • Can we revert the migration?
  • Is there data loss on rollback?
  • Do we need a data backup before migrating?