database-migration-integrity-checker
Database Migration Integrity Checker
Purpose and Intent
The database-migration-integrity-checker is a safety net for your most critical asset: your data. It catches dangerous SQL operations that might pass a standard code review but could cause production outages or data loss.
When to Use
- CI/CD Pipelines: Block deployments if a migration contains a high-risk operation without manual override.
- Local Development: Run before committing a new migration to ensure it follows safe DDL practices.
When NOT to Use
- Data Querying: This is for schema changes, not for auditing standard SELECT/INSERT queries.
Security and Data-Handling Considerations
- Reads SQL files only; no database access required.
- Safe for local use.
More from jorgealves/agent_skills
python-security-scanner
Detect common Python vulnerabilities such as SQL injection, unsafe deserialization, and hardcoded secrets. Use as part of a secure SDLC for Python projects.
175prompt-injection-scanner
Audits agent skill instructions and system prompts for vulnerabilities to prompt hijacking and indirect injection. Use when designing new agent skills or before deploying agents to public environments where users provide untrusted input.
140gdpr-ccpa-privacy-auditor
Audits web applications to ensure declared privacy policies match actual technical data collection practices. Use to identify discrepancies in cookie usage, tracking scripts, and user data handling.
137pii-sanitizer
Detects and redacts Personally Identifiable Information (PII) like emails, phone numbers, and credit cards. Use when cleaning logs, datasets, or communications to comply with GDPR/CCPA privacy standards.
119hipaa-compliance-guard
Audits HealthTech applications for HIPAA technical safeguards like encryption and audit logging. Use when reviewing healthcare infrastructure or ensuring PHI is handled according to legal security standards.
119python-data-pipeline-designer
Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.
116