Skill UI

This skill serves as a comprehensive reference guide for Kling AI's various generative models. It details capabilities across video generation (T2V, I2V), image styling, lip sync, and virtual try-on. Users can compare model versions based on quality, speed, and cost to select the optimal model for their creative or production pipeline.

This skill provides a comprehensive guide to the credit-based pricing model of Kling AI. It details costs for video generation (Standard/Professional modes), image creation (Kolors), and API resource packs. Users can utilize this guide to accurately budget, estimate total project costs, and implement cost-saving strategies, ensuring the efficient and economical execution of their AI content generation workflow.

A production-ready Python SDK providing comprehensive patterns for integrating with the Kling AI API. This wrapper handles crucial operational complexities such as auto-refreshing JWT tokens, structured request/response models, exponential backoff polling for long-running tasks, and detailed error handling. Use this library when building stable, high-reliability applications that require advanced text-to-video or image-to-video synthesis capabilities.

This guide details how to apply sophisticated artistic styles and visual effects to Kling AI video generation. Users can achieve diverse aesthetics—such as Cinematic, Anime, Watercolor, or Cyberpunk—either through detailed prompt engineering or by utilizing the dedicated Effects API (e.g., hug, kiss). It also provides methods for pre-processing images using Kolors for highly consistent and professional video outputs.

LLaVA is an open-source vision-language assistant pairing CLIP vision encoders with Vicuna/LLaMA models to power multimodal chatbots, multi-turn image Q&A, captioning, and instruction-following tasks.

Recommends suitable Nano Banana Pro image generation prompts from a 6000+ library, guiding users through direct image requests or illustrating existing articles, videos, or podcasts with matched prompt templates.

Automate OpenAI API workflows through the Composio MCP integration, covering text and multimodal responses with structured outputs, embeddings, image generation, and model discovery.

Enables Claude to invoke the computer-vision-processor plugin, auto-generating Python calls to run object detection, classification, and segmentation on user-provided images for detailed analysis.

Provides Meta AI’s Segment Anything Model for zero-shot image segmentation via interactive prompts or automatic mask generation, enabling rapid dataset creation and integration in vision pipelines.

Sentence Transformers provides 5000+ pre-trained models that generate sentence, text, and image embeddings for RAG, semantic search, clustering, and multilingual production workloads without relying on external APIs.

A comprehensive guide to leveraging Stable Diffusion models via the HuggingFace Diffusers library. This skill facilitates state-of-the-art image creation, including Text-to-Image generation from prompts, Image-to-Image translation, inpainting, outpainting, and advanced workflow building. Supports various models like SDXL and SD 3.0, ideal for professional creative and computational vision tasks.

A comprehensive guide for deploying Machine Learning training jobs and inference services on the Vast.ai GPU cloud. This skill covers the entire deployment lifecycle, including optimizing Docker images for performance, creating automated provisioning scripts using the Vast.ai CLI, implementing robust data transfer strategies (SCP, rsync), and performing post-deployment health checks.