Claude-code-plugins-plus schema-validator

install

source · Clone the upstream repo

git clone https://github.com/jeremylongshore/claude-code-plugins-plus-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/jeremylongshore/claude-code-plugins-plus-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/11-data-pipelines/schema-validator" ~/.claude/skills/jeremylongshore-claude-code-plugins-plus-schema-validator-fa37cb && rm -rf "$T"

manifest: skills/11-data-pipelines/schema-validator/SKILL.md

source content

Schema Validator

Overview

This skill provides automated assistance for schema validator tasks within the Data Pipelines domain.

When to Use

This skill activates automatically when you:

Mention "schema validator" in your request
Ask about schema validator patterns or best practices
Need help with data pipeline skills covering etl, data transformation, workflow orchestration, and streaming data processing.

Instructions

Provides step-by-step guidance for schema validator
Follows industry best practices and patterns
Generates production-ready code and configurations
Validates outputs against common standards

Examples

Example: Basic Usage Request: "Help me with schema validator" Result: Provides step-by-step guidance and generates appropriate configurations

Prerequisites

Relevant development environment configured
Access to necessary tools and services
Basic understanding of data pipelines concepts

Output

Generated configurations and code
Best practice recommendations
Validation results

Error Handling

Error	Cause	Solution
Configuration invalid	Missing required fields	Check documentation for required parameters
Tool not found	Dependency not installed	Install required tools per prerequisites
Permission denied	Insufficient access	Verify credentials and permissions

Resources

Official documentation for related tools
Best practices guides
Community examples and tutorials

Related Skills

Part of the Data Pipelines skill category. Tags: etl, airflow, spark, streaming, data-engineering