Skill UI

A comprehensive toolkit for evaluating the performance of code generation models. It supports benchmarking against industry standards like HumanEval, MBPP, and MultiPL-E across multiple languages. Use this tool to quantitatively compare different LLM coding abilities and measure code synthesis quality using pass@k metrics.

This skill provides capabilities for bidirectional audio processing using fal.ai models. It supports Text-to-Speech (TTS), converting written text into natural-sounding speech, and Speech-to-Text (STT), accurately transcribing spoken audio into readable text. Use this skill for applications requiring high-quality audio input analysis or synthetic voice generation, such as virtual assistants, content digitization, or multilingual communication tools.

This skill enables the generation of high-quality images and videos using advanced fal.ai AI models. It is designed for creative professionals, artists, and developers who require powerful, generative visual content. Use this when your task involves transforming text prompts into sophisticated visual media, making it ideal for concept art, multimedia prototyping, or advanced content creation.

This guide details a production-ready reference architecture for applications built on Groq's LPU inference API. It covers sophisticated model routing based on latency, quality, or cost needs, implementing streaming pipelines, and establishing robust multi-provider fallback chains (e.g., Groq primary to OpenAI backup). Ideal for designing and reviewing complex, resilient LLM systems.

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.

Integrate Langfuse into CI/CD pipelines (e.g., GitHub Actions) to automate the testing and validation of Large Language Models (LLMs). This guide covers prompt regression testing, tracing validation, and setting up experiment-driven quality gates, ensuring reliable and high-performing AI applications throughout the development lifecycle.

This skill guides the design and implementation of a complete, production-grade Machine Learning pipeline. It employs a multi-agent orchestration workflow, systematically covering all stages of the ML lifecycle: data ingestion and quality assurance, feature engineering, model training, optimization, and robust deployment (MLOps). It ensures built-in reproducibility, monitoring, and scalability.

Automate the validation and testing of prompts and AI responses within your CI/CD pipeline using Mistral AI. This integration provides robust prompt regression testing, model quality checks, automatic cost estimation for API usage, and deployment gates, ensuring the stability and reliability of your AI features upon every pull request.

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.

A comprehensive collection of battle-tested, structured prompts designed to elevate interactions with Large Language Models (LLMs). Covers advanced use cases including expert coding reviews, system architecture design, performance analysis, technical writing, and creative brainstorming, ensuring high-quality, actionable AI output.

A comprehensive, model-agnostic toolkit for advanced LLM development. It enables iterative prompt optimization through static analysis (token usage, clarity), measures critical RAG metrics (relevance, faithfulness, coverage), and validates complex agent workflows and configurations. Essential for rigorous LLM quality assurance and cost management.

Speeds up LLM inference by combining speculative decoding with Medusa multiple heads and lookahead techniques so chatbots, code assistants, and other real-time workloads can run with 1.5-3.6× lower latency while preserving target-model quality.