klingai-rate-limits
Handle Kling AI rate limits with proper backoff strategies. Use when experiencing 429 errors or building high-throughput systems. Trigger with phrases like 'klingai rate limit', 'kling ai 429', 'klingai throttle', 'klingai backoff'. 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 Rate Limits
## Overview
This skill teaches rate limit handling patterns including exponential backoff, token bucket algorithms, request queuing, and concurrent job management for reliable Kling AI integrations.
## Prerequisites
- Kling AI integration
- Understanding of HTTP status codes
- Python 3.8+ or Node.js 18+
## Instructions
Follow these steps to handle rate limits:
1. **Understand Limits**: Know the rate limit structure
2. **Implement Detection**: Detect rate limit responses
3. **Add Backoff**: Implement exponential backoff
4. **Queue Requests**: Add request queuing
5. **Monitor Usage**: Track rate limit consumption
## Output
Successful execution produces:
- Rate limit handling without errors
- Smooth request throughput
- Proper backoff behavior
- Concurrent job management
## Error Handling
See `{baseDir}/references/errors.md` for comprehensive error handling.
## Examples
See `{baseDir}/references/examples.md` for detailed examples.
## Resources
- [Kling AI Rate Limits](https://docs.klingai.com/rate-limits)
- [Exponential Backoff](https://cloud.google.com/iot/docs/how-tos/exponential-backoff)
- [Token Bucket Algorithm](https://en.wikipedia.org/wiki/Token_bucket)