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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
• Optimization Best Practices
• Caching Strategies