Skill UI

Feature Engineering Toolkit automates creating, selecting, and transforming dataset features to improve machine learning models; use it whenever you need new interactions, automatic selection, scaling, or encoding to boost accuracy and interpretability.

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.

Practical guide to integrating Claude models: covers API client usage, streaming responses, tool calls, RAG pipelines, document chunking, cost-aware model selection, and anti-pattern warnings for efficient LLM workflows.

This skill provides advanced strategies for managing large language model (LLM) context windows and token usage via OpenRouter. Functions include context window lookup, context-aware model selection, conversation trimming, document chunking for RAG, and prompt caching. Use this when dealing with long conversations or large documents to maximize quality while minimizing API costs and preventing context limit overflow.

This comprehensive guide provides step-by-step instructions for integrating the Google Gemini API into applications using Python and Node.js. It covers the entire lifecycle, from basic text generation to advanced use cases like multimodal inputs (text + image), real-time streaming responses, function calling/tool use, and multi-turn chat. Includes best practices for model selection, error handling, and production deployment.

This skill is designed for performing local, on-premise evaluations and benchmarking of models hosted on the Hugging Face Hub. It facilitates running specialized evaluation frameworks like `inspect-ai` and `lighteval` against local hardware. Users can select optimal inference backends, including `vllm` for high throughput, or fall back to Hugging Face Transformers or `accelerate`, ensuring comprehensive smoke testing and task selection for LLMs. It focuses purely on the local execution process, separate from remote jobs or publishing workflows.

Provides strategies to optimize costs when utilizing the Cohere API. This guide covers model tier selection (economy, standard, premium), implementing token budgets, reducing embedding storage costs (e.g., using int8), and establishing comprehensive usage trackers. It is essential for maintaining cost predictability in production AI deployments.

A comprehensive guide for optimizing Cohere API v2 performance. Learn advanced strategies to improve throughput, reduce latency, and manage costs when using Chat, Embed, and Rerank endpoints. Techniques covered include dynamic model selection based on latency budgets, implementing streaming for faster perceived response times, efficient batch processing for embeddings, vector compression, and robust caching mechanisms.

This skill provides methods to optimize ElevenLabs usage costs for Text-to-Speech (TTS) workloads. It covers model selection (e.g., using Flash/Turbo for savings), implementing character-efficient text pre-processing to reduce character count, and setting up real-time quota monitoring with budget alerts. Ideal for developers managing high-volume, cost-sensitive voice applications.

This comprehensive guide details advanced workflows for high-quality video generation using platforms like Runway. Learn essential techniques including advanced prompt engineering (Subject + Action + Setting + Style), optimal model selection (e.g., gen4_turbo), and parameter tuning for duration and aspect ratio. The workflow also demonstrates efficient batch generation for creating multiple, consistent video assets.