A comprehensive toolkit for enforcing data integrity using schemas across multiple languages and file formats.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-validation
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install data-validation using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Data Validation?
The Data Validation skill is designed to provide developers with a robust framework for defining and enforcing data structures. By utilizing industry standards like JSON Schema alongside modern libraries such as Zod for TypeScript and Pydantic for Python, this collection of Openclaw Skills ensures that data remains consistent as it moves across system boundaries. It bridges the gap between different environments, allowing for seamless data contracts between services.
Beyond simple type checking, this skill focuses on practical data engineering tasks. It includes specialized workflows for validating API request and response bodies, checking the structural integrity of CSV and JSON files, and verifying that data migrations preserve record counts and values. It is an essential asset for maintaining high-quality, bug-free data pipelines in professional development environments.
Data Validation Use Cases
- Defining the shape of API request and response bodies to prevent runtime errors.
- Validating user input at the edge before it reaches core business logic.
- Setting up strict data contracts between microservices to ensure compatibility.
- Checking CSV and JSON file integrity before performing bulk data imports.
- Verifying ETL processes and data migrations to ensure zero data loss.
- Generating automated documentation and TypeScript types directly from validation schemas.
How Data Validation Works
- Define a schema or model using the preferred library (Zod for Node.js/TypeScript, Pydantic for Python, or standard JSON Schema).
- Integrate the validation logic at system boundaries, such as API middleware, file upload handlers, or message queue consumers.
- Execute the validation process to check incoming data against the defined constraints like required fields, regex patterns, or numeric ranges.
- Use Openclaw Skills command-line utilities like jq or ajv-cli for rapid ad-hoc validation of static files and datasets.
- Handle the validation output by either proceeding with type-safe data or returning structured error messages for debugging.
Data Validation Setup
To begin using these Openclaw Skills, ensure your environment has the necessary runtimes. Install the validation libraries relevant to your stack:
# For Node.js environments
npm install zod ajv
# For Python environments
pip install pydantic jsonschema email-validator
# For CLI-based validation
sudo apt install jq # Or brew install jq
Data Validation Data Schema & Taxonomy
This skill organizes data validation through various schema formats, enabling interoperability between different programming languages.
| Data Format | Validation Method | Key Features |
|---|---|---|
| JSON | JSON Schema (Draft 2020-12) | Cross-platform, draft-compliant, highly portable |
| TypeScript | Zod Schemas | Type inference, safe parsing, async validation |
| Python | Pydantic Models | Type coercion, strict mode, FastAPI integration |
| CSV | Bash/AWK Scripts | Column count consistency, duplicate detection, empty field checks |
| Migration | Python Validation Script | Source vs. Target record comparison and field-level diffing |
Data Validation Advanced Features
- Discriminated Unions: Support for complex tagged union types in both Zod and Pydantic for polymorphic data handling.
- Data Transformation: Clean and normalize data (e.g., trimming strings, coercing types) during the validation phase.
- Recursive Schema Support: Define nested data structures like category trees or file systems with ease.
- Strict Mode Enforcement: Disable implicit type coercion to ensure data matches the expected format exactly.
- Cross-Language Schema Generation: Export JSON Schemas from Zod or Pydantic definitions to share contracts across diverse tech stacks using Openclaw Skills.
- Custom Refinements: Implement complex validation logic that goes beyond simple type checks, such as password strength or date range logic.
SKILL.md
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