Database Migration Guard Skill

This skill automatically activates when Claude detects changes to database schema or migration files.

Trigger Criteria

Migration File Patterns (Immediate Trigger)

Activate when files matching these patterns are modified:

Django (Python):

•*/migrations/*.py
•models.py files

SQLAlchemy (Python):

•*/alembic/versions/*.py
•alembic/env.py

Rails (Ruby):

•db/migrate/*.rb
•db/schema.rb

Node.js (Sequelize, Knex, TypeORM):

•*/migrations/*.js, */migrations/*.ts
•*/models/*.js, */models/*.ts

Go (golang-migrate, GORM):

•*/migrations/*.sql
•*/migrations/*.go

Behavior

When triggered, this skill performs lightweight validation:

•Check migration safety - Breaking vs non-breaking changes
•Check backwards compatibility - Can old code run with new schema?
•Check data migration - Is there a plan for existing data?
•Check rollback strategy - Can this migration be reversed?
•Check index strategy - Large tables use CONCURRENT index creation
•Report inline - Non-blocking feedback

Analysis Checks

1. Breaking vs Non-Breaking Changes

Non-breaking (safe to deploy immediately):

•✅ Add new table
•✅ Add new column (with DEFAULT or NULL)
•✅ Add new index (CONCURRENTLY for PostgreSQL)
•✅ Add new constraint (NOT VALID, then validate separately)

Breaking (requires multi-phase deployment):

•⚠️ Rename column (needs dual-write period)
•⚠️ Remove column (needs dual-read period first)
•⚠️ Change column type (needs backfill)
•⚠️ Add NOT NULL constraint without DEFAULT
•❌ Remove table (must ensure no code references it)

Check: Flag breaking changes and recommend multi-phase approach.

2. Backwards Compatibility

Verify that old application code can run with the new schema:

Good example (backwards compatible):

python

# Migration: Add new column with DEFAULT
class Migration:
    def upgrade():
        op.add_column('users', sa.Column('age', sa.Integer(), nullable=True, server_default='0'))

Old code doesn't know about age column → still works ✅

Bad example (breaks old code):

python

# Migration: Add NOT NULL column without DEFAULT
class Migration:
    def upgrade():
        op.add_column('users', sa.Column('age', sa.Integer(), nullable=False))

Old code tries to INSERT without age → fails ❌

Multi-phase approach for breaking changes:

python

# Phase 1: Add nullable column
op.add_column('users', sa.Column('age', sa.Integer(), nullable=True))

# Deploy new code that writes to both old and new columns

# Phase 2: Backfill data
op.execute('UPDATE users SET age = 0 WHERE age IS NULL')

# Deploy new code that only uses new column

# Phase 3: Make NOT NULL
op.alter_column('users', 'age', nullable=False)

# Phase 4: Remove old column (if any)
op.drop_column('users', 'old_age_field')

Check:

•New columns are nullable or have defaults
•Renamed/removed columns use multi-phase approach
•Migration comments explain phasing

3. Data Migration Strategy

For migrations that transform existing data:

Required elements:

•Data backfill script
•Rollback plan
•Performance estimate (for large tables)
•Batching strategy (for >100k rows)

Good example:

python

# Migration: Normalize phone numbers
class Migration:
    def upgrade():
        # 1. Add new column
        op.add_column('users', sa.Column('phone_normalized', sa.String(20), nullable=True))

        # 2. Backfill in batches
        batch_size = 1000
        op.execute('''
            UPDATE users
            SET phone_normalized = regexp_replace(phone, '[^0-9]', '', 'g')
            WHERE id IN (
                SELECT id FROM users
                WHERE phone_normalized IS NULL
                LIMIT {batch_size}
            )
        '''.format(batch_size=batch_size))

        # 3. Make NOT NULL after backfill
        op.alter_column('users', 'phone_normalized', nullable=False)

    def downgrade():
        op.drop_column('users', 'phone_normalized')

Check:

•Batching used for large tables
•Rollback script provided
•Data transformation logic tested

4. Index Creation Strategy

For large tables (>100k rows), use concurrent index creation to avoid locking:

PostgreSQL - Good:

sql

CREATE INDEX CONCURRENTLY idx_users_email ON users(email);

PostgreSQL - Bad (locks table during creation):

sql

CREATE INDEX idx_users_email ON users(email);

MySQL - Good:

sql

ALTER TABLE users ADD INDEX idx_users_email (email), ALGORITHM=INPLACE, LOCK=NONE;

Check:

•Large tables (>100k rows) use CONCURRENT or ONLINE options
•Migration comments note expected index creation time

5. Constraint Safety

Adding constraints requires care to avoid locking tables:

PostgreSQL - Good (two-phase approach):

sql

-- Phase 1: Add constraint as NOT VALID (no table scan, fast)
ALTER TABLE users ADD CONSTRAINT users_email_check CHECK (email ~* '@') NOT VALID;

-- Phase 2: Validate in separate transaction (can be cancelled if needed)
ALTER TABLE users VALIDATE CONSTRAINT users_email_check;

PostgreSQL - Bad (locks table during full scan):

sql

ALTER TABLE users ADD CONSTRAINT users_email_check CHECK (email ~* '@');

Check:

•NOT NULL constraints have DEFAULT values
•CHECK constraints use NOT VALID + VALIDATE pattern
•Foreign keys use NOT VALID + VALIDATE pattern

6. Rollback Strategy

Every migration should have a tested rollback plan:

Required:

•downgrade() or down() function implemented
•Rollback tested on staging
•Data loss risk documented (if any)

Example:

python

class Migration:
    def upgrade():
        op.add_column('users', sa.Column('age', sa.Integer(), nullable=True))

    def downgrade():
        # SAFE: Column removal doesn't break old code
        # WARNING: Data in 'age' column will be lost
        op.drop_column('users', 'age')

Check:

•Downgrade function present
•Data loss risk documented
•Rollback tested

Validation Output

text

🗄️ Database Migration Guard Findings:

Migration: 20260204_add_user_age.py

✅ Backwards compatibility: PASS
   - New column 'age' is nullable
   - Old code can run with new schema

⚠️  Data migration: WARNING
   - No backfill script for existing rows
   - Recommendation: Add default value or backfill script

✅ Index strategy: PASS
   - Using CREATE INDEX CONCURRENTLY
   - Estimated time: 30s for 500k rows

❌ Rollback: FAIL
   - No downgrade() function
   - Add rollback script before deploying

Deployment recommendation: MULTI-PHASE
  Phase 1: Deploy migration (add nullable column)
  Phase 2: Deploy code that writes to new column
  Phase 3: Backfill existing data
  Phase 4: Make column NOT NULL (if needed)

Common Patterns

Safe Column Rename (3-phase)

python

# Phase 1: Add new column, dual-write
op.add_column('users', sa.Column('full_name', sa.String(), nullable=True))
# Deploy code that writes to both 'name' and 'full_name'

# Phase 2: Backfill
op.execute('UPDATE users SET full_name = name WHERE full_name IS NULL')
# Deploy code that reads from 'full_name', writes to both

# Phase 3: Remove old column
op.drop_column('users', 'name')
# Deploy code that only uses 'full_name'

Safe Column Removal (2-phase)

python

# Phase 1: Stop writing to column
# Deploy code that doesn't write to 'deprecated_field'

# Phase 2: Remove column
op.drop_column('users', 'deprecated_field')
# Verify no code references the field

Safe Type Change (4-phase)

python

# Phase 1: Add new column
op.add_column('users', sa.Column('age_int', sa.Integer(), nullable=True))

# Phase 2: Dual-write
# Deploy code that writes to both 'age' (string) and 'age_int' (integer)

# Phase 3: Backfill
op.execute('UPDATE users SET age_int = CAST(age AS INTEGER) WHERE age_int IS NULL')
# Deploy code that reads from 'age_int', writes to both

# Phase 4: Remove old column
op.drop_column('users', 'age')

Configuration

yaml

skills:
  database-migration-guard:
    enabled: true
    large_table_threshold: 100000  # Rows
    require_concurrent_indexes: true
    require_rollback: true

•templates/skills/api-security-guard - API endpoint validation
•templates/validation-overrides/database-safety.yml - Database-specific rules

Database Migration Guard Skill

Trigger Criteria

Migration File Patterns (Immediate Trigger)

Behavior

Analysis Checks

1. Breaking vs Non-Breaking Changes

2. Backwards Compatibility

3. Data Migration Strategy

4. Index Creation Strategy

5. Constraint Safety

6. Rollback Strategy

Validation Output

Common Patterns

Safe Column Rename (3-phase)

Safe Column Removal (2-phase)

Safe Type Change (4-phase)

Configuration

Related