Validate data with schemas across languages and formats. Use when defining JSON Schema, using Zod (TypeScript) or Pydantic (Python), validating API request/response shapes, checking CSV/JSON data integrity, or setting up data contracts between services.
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Select your agent
Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install data-validationOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install data-validationβ οΈ Requires Node.js 18+. No Node? Use Option 2 below to download the ZIP instead. Install Node.js β
Option 2: Manual install (no Node required)
Download the ZIP, extract it, and place the folder at the path below. Restart your agent to activate.
Install path
~/.claude/skills/data-validation/π‘Extract and place the folder at the path above, then restart your agent.
Category
π»Developer & DevOpsPlatforms
What Data Validation can do for your AI workflow
Validate data with schemas directly from your Claude conversation
Works across Claude, Cursor, OpenClaw β install once, use everywhere
Trusted by 2,262+ developers worldwide
One-command installation β no complex setup required
Combine with other skills to build powerful multi-step AI workflows
Try these prompts with your AI agent after installing Data Validation
Help me get started with Data Validation
Explains what Data Validation does, walks through the setup, and runs a quick demo based on your current project
Use Data Validation to validate data with schemas across languages and formats
Invokes Data Validation with the right parameters and returns the result directly in the conversation
What can I do with Data Validation in my developer & devops workflow?
Lists the top use cases for Data Validation, with example commands for each scenario
Guides & tutorials for AI skills
Data Validation extends your AI assistant with the ability to validate data with schemas across languages and formats. Use when defining JSON Schema, using Zod (TypeScript) or Pydantic (Python), validating API request/response shapes, checking CSV/JSON data integrity, or setting up data contracts between services. Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly β and it will take care of the task end-to-end, using Data Validation as its underlying capability.
Data Validation works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) β an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install Data Validation once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.
To install Data Validation, copy the skill folder to `~/.claude/skills/data-validation/` for use across all your projects, or `.claude/skills/data-validation/` for a single project. Restart Claude and the skill is immediately active β invoke it with `/data-validation` or just describe your goal and the AI will pick it up automatically.
Data Validation has been installed 2,262 times, making it one of the more actively used skills in the Developer & DevOps category. The install rate suggests it solves a real, recurring need rather than a niche edge case. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.
Place the skill folder at ~/.claude/skills/data-validation/ for personal use (all projects), or .claude/skills/data-validation/ for project-specific use. Restart your AI client, then invoke with /data-validation or let the AI discover it automatically.
Data Validation supports Claude, Cursor, OpenClaw. It integrates seamlessly with these AI platforms to extend their capabilities.
Data Validation is free to install. Check the repository for licensing information.
Validate data with schemas across languages and formats. Use when defining JSON Schema, using Zod (TypeScript) or Pydantic (Python), validating API request/response shapes, checking CSV/JSON data integrity, or setting up data contracts between services.
Automate my developer & devops tasks using Data Validation
Identifies repetitive steps in your workflow and sets up Data Validation to handle them automatically
MCP vs traditional plugins: what's the difference?
Data Validation is categorized under Developer & DevOps. These skills help AI agents perform specialized tasks in this domain.