playbooks

home / skills / jeremylongshore / claude-code-plugins-plus-skills / schema-validator

schema-validator skill

/skills/11-data-pipelines/schema-validator

This skill helps you implement and validate schema validator configurations for data pipelines with step-by-step guidance and production-ready code.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill schema-validator

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
2.1 KB
---
name: "schema-validator"
description: |
  Validate schema validator operations. Auto-activating skill for Data Pipelines.
  Triggers on: schema validator, schema validator
  Part of the Data Pipelines skill category. Use when working with schema validator functionality. Trigger with phrases like "schema validator", "schema validator", "schema".
allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# 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

1. Provides step-by-step guidance for schema validator
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. 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

Overview

This skill provides automated assistance for schema validator tasks in data pipelines. It helps design, test, and enforce input/output schema rules across ETL, streaming, and workflow orchestration components. Use it to generate production-ready validators, configurations, and actionable remediation steps.

How this skill works

The skill inspects schema definitions, sample data, and pipeline configurations to detect mismatches, missing fields, type errors, and structural drift. It can generate validation code (examples in Python), configuration snippets for common tools, and a step-by-step remediation plan. It also runs basic checks against common standards and returns a clear list of failures and suggested fixes.

When to use it

  • When you mention or ask about schema validator functionality in a data pipeline
  • When onboarding new data sources or preparing schema contracts with producers/consumers
  • When adding schema enforcement to ETL jobs, streaming jobs, or orchestration workflows
  • When validating outputs after transformations or during CI/CD checks
  • When diagnosing data quality alerts tied to schema changes

Best practices

  • Define explicit, versioned schema contracts for each input and output stream or table
  • Prefer schema evolution rules (backward/forward compatibility) and document allowed changes
  • Integrate schema checks into CI pipelines and pre-deployment gates
  • Use automated tests with representative sample data to catch edge cases early
  • Log validation failures with clear error codes and include remediation guidance

Example use cases

  • Create a JSON/Avro/Parquet schema validator for an ingestion pipeline and generate sample validator code
  • Validate transformation outputs in an Airflow task and produce a remediation checklist when mismatches occur
  • Generate CI job configuration that fails builds on schema-breaking changes
  • Produce data contract documentation and compatibility guidance for producers and consumers
  • Diagnose a streaming job failure caused by a schema drift and suggest fixes

FAQ

What inputs do I need to provide for validation?

Provide the schema (JSON Schema, Avro, or a typed spec), representative sample records, and the pipeline component configuration to validate against.

Can it generate code for my environment?

Yes. It can produce example Python validation code and configuration snippets tailored to common frameworks like Spark, Airflow, and streaming connectors.