Data Validation

**Judgement:** This skill is a high-quality reference for schema-based data validation and data integrity checking across JSON Schema, Zod, and Pydantic, plus practical CLI recipes for CSV/JSON and migration validation. It is best used as a *design and implementation aid* rather than an automated validator. **Strengths & what it’s good for** - Excellent, concrete examples for: - JSON Schema (including conditional logic, patternProperties, reusable `$defs`, `additionalProperties: false`). - Zod: basic/advanced schemas, transforms, discriminated unions, recursive types, refinements, API middleware patterns. - Pydantic v2-style models: field/model validators, strict mode, aliases, discriminated unions, computed fields, FastAPI integration. - Data integrity scripts: CSV validation via bash/awk, JSON checks with `jq`, and a Python migration checker. - Strong emphasis on **validating at system boundaries** and on **data contracts** between services. - Snippets are realistic and immediately reusable in typical web/API and data-engineering workflows. **Risks / limitations** - It does *not* perform validation by itself; it provides patterns and snippets. The model must still reason correctly, adapt examples, and avoid copy-paste errors. - Version drift risk: details (especially for Pydantic v2 and Zod APIs) may eventually diverge from current library versions; callers should verify against installed versions. - Misapplied strictness (e.g., `additionalProperties: false`, strict Pydantic models) can cause legitimate data to be rejected if used without considering backward compatibility. - CLI examples assume tools like `ajv-cli`, `jq`, and Unix utilities (`awk`, `sort`, `wc`) are available; on constrained or non-Unix environments these commands may not work as-is. **Recommended scenarios** - Designing or refining **API request/response schemas** and enforcing them across microservices. - Implementing **input validation** for web backends using Express+Zod or FastAPI+Pydantic. - Establishing **data contracts** between TypeScript and Python services using JSON Schema as the shared language. - Building **ETL and migration checks** to ensure record counts, IDs, and critical fields are preserved across systems. - Pre-flight **CSV/JSON integrity checks** before bulk import into databases, warehouses, or analytics pipelines.