data-validation-rules

name	data-validation-rules
description	Data Validation Rules are constraints and checks that ensure data meets\ Use when this capability is needed.
metadata	{"author":"amnadtaowsoam"}

Data Validation Rules

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

Data Validation Rules are constraints and checks that ensure data meets quality standards before being stored or processed. Validation should happen at multiple layers—database, application, API, and pipeline—to create defense in depth.

Core Principle: "Validate early, validate often. Never trust user input or upstream data."

This skill provides comprehensive guidance on implementing validation rules across all layers of the data stack.

Why This Matters

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Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • <e.g., env vars, request payload, file paths, schema>
• Entry Conditions:
  • <Pre-requisites: e.g., Repo initialized, DB running, specific branch checked out>
• Outputs:
  • <e.g., artifacts (PR diff, docs, tests, dashboard JSON)>
• Artifacts Required (Deliverables):
  • <e.g., Code Diff, Unit Tests, Migration Script, API Docs>
• Acceptance Evidence:
  • <e.g., Test Report (screenshot/log), Benchmark Result, Security Scan Report>
• Success Criteria:
  • <e.g., p95 < 300ms, coverage ≥ 80%>

Skill Composition

• Depends on: None
• Compatible with: None
• Conflicts with: None
• Related Skills: None

Quick Start

Assumptions

• Validation rules can be defined declaratively
• Team has capacity to maintain validation logic
• Performance requirements allow for validation overhead
• Error messages can be made user-friendly

Compatibility

• Works with any programming language
• Compatible with all major databases
• Framework-agnostic approach
• Adaptable to different validation libraries

Test Scenario Matrix

Scenario	Test Case	Expected Outcome
Required field	Submit without required field	Validation error with clear message
Type mismatch	Submit string for number field	Validation error
Invalid format	Submit invalid email	Validation error
Out of range	Submit age = 200	Validation error
Invalid enum	Submit invalid status	Validation error
Cross-field	End date before start date	Validation error

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives

1. Validate everywhere - At database, application, API, and pipeline
2. Fail fast - Reject invalid data as early as possible
3. Meaningful errors - Provide clear, actionable error messages
4. Test validation - Validation rules need testing too
5. Document rules - Make validation visible to all

Definition of Done

Data validation implementation is complete when:

• Validation rules defined for all data types
• Validation implemented at all layers (DB, app, API, pipeline)
• Error messages are clear and actionable
• Validation rules are tested
• Performance is optimized for batch operations
• Documentation is complete
• API consumers are informed of validation rules
• Monitoring is in place for validation failures
• Regular review schedule established
• Team trained on validation procedures

Anti-patterns

1. Validating only at one layer - Need defense in depth
2. Silent failures - Always return validation errors
3. Generic error messages - Be specific about what's wrong
4. Trusting upstream data - Validate everything
5. Complex validation logic - Keep it simple and maintainable

Reference Links

Versioning

This skill follows semantic versioning (MAJOR.MINOR.PATCH):

• MAJOR: Breaking changes to procedures or standards
• MINOR: New validation methods or significant enhancements
• PATCH: Bug fixes or documentation updates

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