Skill UI

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.

Generates complete, production-ready Google Cloud Platform (GCP) code examples and infrastructure templates. It spans various modern AI patterns, including ADK agents, Genkit RAG flows, and multimodal analysis using Vertex AI and Gemini. The output includes runnable code, deployment configuration (Terraform/IaC), security best practices (IAM, VPC-SC), and monitoring setup, ensuring enterprise-grade reliability and cost optimization.

GPTQ enables post-training 4-bit quantization for large LLMs, delivering up to 4× memory reduction and 3-4× faster inference with under 2% perplexity loss on consumer GPUs through AutoGPTQ, PEFT, and Transformer integrations.

A comprehensive guide to using Groq's high-speed LPU for chat completion. This resource provides code examples in TypeScript and Python, covering basic chat interactions, streaming responses, and advanced multimodal features. Ideal for developers building high-performance, low-latency AI applications using the Groq API.

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 provides structured patterns for combining vector similarity search with traditional keyword-based search. It is essential for building advanced Retrieval-Augmented Generation (RAG) systems, sophisticated search engines, or any retrieval task requiring high recall. Use this when pure semantic understanding is insufficient, and precise matching of specific terms (like names or codes) is necessary.

This skill provides advanced secondary workflows for Ideogram, enabling comprehensive image manipulation beyond simple text-to-image generation. Key functions include Magic Fill (inpainting/editing), Remix (style transfer), Upscaling, describing image content (image-to-text), and re-framing. Ideal for professional image refinement, style transfer, and content enhancement.

A comprehensive guide demonstrating how to generate your first AI image using the Ideogram API. It provides runnable examples in cURL, TypeScript, and Python, covering both legacy and V3 endpoints. Users will learn essential API parameters, such as prompt structuring, style types, and aspect ratios, making it ideal for initial integration testing and setup.

Instructor lets you shape LLM responses into validated Pydantic models, automatically retrying failed extractions, parsing complex JSON with type safety, and streaming partial outputs across Anthropic, OpenAI, and other providers.

This skill guides the installation and configuration of the Juicebox PeopleGPT API. It enables developers to access AI-powered people search capabilities across millions of professional profiles. Use this when setting up the initial connection or performing deep people search functionality within your application.

Implements robust content policy compliance for generative AI prompts and outputs, such as those from Kling AI. This skill provides pre-submission filtering functionality, preventing the generation of restricted content (e.g., violence, explicit material, hate speech) and saving API credits. It includes Python code examples for prompt screening and handling server-side rejection messages.