---
name: klingai-reference-architecture
description: |
Execute production-ready reference architecture for Kling AI video platforms. Use when designing
scalable video generation systems. Trigger with phrases like 'klingai architecture',
'kling ai system design', 'video platform architecture', 'klingai production setup'.
allowed-tools: Read, Write, Edit, Grep
version: 1.0.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
---
# Klingai Reference Architecture
## Overview
This skill provides production-ready reference architectures for building scalable video generation platforms using Kling AI, including microservices design, event-driven patterns, and infrastructure recommendations.
## Prerequisites
- Understanding of distributed systems
- Cloud infrastructure experience (AWS/GCP/Azure)
- Docker/Kubernetes knowledge helpful
## Instructions
Follow these steps to design your architecture:
1. **Choose Pattern**: Select appropriate architecture pattern
2. **Design Components**: Map out service boundaries
3. **Plan Infrastructure**: Choose cloud services
4. **Implement Resilience**: Add fault tolerance
5. **Monitor & Scale**: Set up observability
## Output
Successful execution produces:
- Scalable video generation platform
- Event-driven processing pipeline
- Container-ready deployment configs
- Auto-scaling based on queue depth
## Error Handling
See `{baseDir}/references/errors.md` for comprehensive error handling.
## Examples
See `{baseDir}/references/examples.md` for detailed examples.
## Resources
- [Kling AI API](https://docs.klingai.com/api)
- [Kubernetes Documentation](https://kubernetes.io/docs/)
- [Redis Queues](https://redis.io/docs/data-types/lists/)
