klingai-reference-architecture
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>
Allowed Tools
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Provided by Plugin
klingai-pack
Kling AI skill pack - 30 skills for AI video generation, image-to-video, text-to-video, and production workflows
Installation
This skill is included in the klingai-pack plugin:
/plugin install klingai-pack@claude-code-plugins-plusClick to copy
Instructions
# 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/)