Why Use This
This skill provides specialized capabilities for jeremylongshore's codebase.
Use Cases
- Developing new features in the jeremylongshore repository
- Refactoring existing code to follow jeremylongshore standards
- Understanding and working with jeremylongshore's codebase structure
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Updated At Jan 11, 2026, 10:30 PM
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SKILL.md 56 Lines
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Total Size 0 B
License MIT
---
name: klingai-rate-limits
description: |
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 <[email protected]>
---
# 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)
