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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License MIT
---
name: klingai-performance-tuning
description: |
Optimize Kling AI performance for speed and quality. Use when improving generation times,
reducing costs, or enhancing output quality. Trigger with phrases like 'klingai performance',
'kling ai optimization', 'faster klingai', 'klingai quality settings'.
allowed-tools: Read, Write, Edit, Grep
version: 1.0.0
license: MIT
author: Jeremy Longshore <[email protected]>
---
# Klingai Performance Tuning
## Overview
This skill demonstrates optimizing Kling AI for better performance including faster generation, improved quality, cost optimization, and efficient resource usage.
## Prerequisites
- Kling AI API key configured
- Understanding of performance tradeoffs
- Python 3.8+
## Instructions
Follow these steps for performance tuning:
1. **Benchmark Baseline**: Measure current performance
2. **Identify Bottlenecks**: Find slow areas
3. **Apply Optimizations**: Implement improvements
4. **Measure Results**: Compare before/after
5. **Balance Tradeoffs**: Find optimal settings
## Output
Successful execution produces:
- Performance benchmarks
- Optimization recommendations
- Configuration comparisons
- Cached generation results
## Error Handling
See `{baseDir}/references/errors.md` for comprehensive error handling.
## Examples
See `{baseDir}/references/examples.md` for detailed examples.
## Resources
- [Kling AI Performance](https://docs.klingai.com/performance)
- [Optimization Best Practices](https://docs.klingai.com/best-practices)
- [Caching Strategies](https://cachetools.readthedocs.io/)
