data-validation

What This Skill Does

The data-validation skill is a robust, multi-language toolkit designed to ensure data integrity across the entire application lifecycle. Whether you are defining complex JSON schemas, writing type-safe TypeScript models with Zod, or enforcing data contracts in Python via Pydantic, this skill provides the structure required to maintain high-quality data standards. By automating the validation of API request and response shapes, this tool prevents bad data from ever entering your backend systems, effectively acting as a first line of defense against malformed inputs and schema drift.

Installation

To install this skill, run the following command in your terminal within the OpenClaw environment: clawhub install openclaw/skills/skills/gitgoodordietrying/data-validation

Use Cases

• API Boundary Enforcement: Automatically validate incoming JSON payloads against strict OpenAPI-compliant schemas before execution.
• Data Migration & ETL: Verify that records imported from CSV or legacy databases conform to your target database model during migration tasks.
• Type Safety Synchronization: Generate or verify TypeScript definitions alongside your server-side models to ensure frontend and backend parity.
• Configuration Management: Validate application configuration files (YAML/JSON) to catch errors before deployments cause runtime failures.
• User Input Sanitization: Standardize user-submitted forms by enforcing patterns, range constraints, and format checks at the agent layer.

Example Prompts

1. "Validate this JSON payload against my existing user-profile schema and highlight any missing required fields or type mismatches."
2. "Write a Zod schema for a product object that includes an array of tags, a numeric price with a minimum of 0.01, and an optional description string."
3. "Convert this Pydantic class into a standard JSON Schema file so I can use it for cross-language validation between my Python backend and Node.js frontend."

Tips & Limitations

• Context is Key: Always provide the full context of your existing codebase when requesting schema generation to ensure compatibility with your current stack.
• Performance: While highly effective, complex schema validation can add latency; consider caching results for large static datasets.
• Schema Drift: Periodically audit your generated schemas against your database state to ensure your validation rules remain accurate.
• Limit: This skill focuses on structural validation. Business logic validation (e.g., checking if a user ID actually exists in the database) should still be handled by your application's service layer rather than schema validation alone.