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'.
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
skills listSkill Instructions
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 jeremy@intentsolutions.io
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:
- Benchmark Baseline: Measure current performance
- Identify Bottlenecks: Find slow areas
- Apply Optimizations: Implement improvements
- Measure Results: Compare before/after
- 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
More by jeremylongshore
View allRabbitmq Queue Setup - Auto-activating skill for Backend Development. Triggers on: rabbitmq queue setup, rabbitmq queue setup Part of the Backend Development skill category.
evaluating-machine-learning-models: This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".
building-neural-networks: This skill allows Claude to construct and configure neural network architectures using the neural-network-builder plugin. It should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance with defining the layers, parameters, and training process. The skill is triggered by requests involving terms like "build a neural network," "define network architecture," "configure layers," or specific mentions of neural network types (e.g., "CNN," "RNN," "transformer").
Oauth Callback Handler - Auto-activating skill for API Integration. Triggers on: oauth callback handler, oauth callback handler Part of the API Integration skill category.
