Skill UI

This guide demonstrates how to implement robust, real-time, low-latency streaming transcription using Deepgram's WebSocket API. It covers advanced audio handling, including microphone input capture (via Sox), speaker diarization, utterance boundary detection, and implementing automatic reconnection logic. Essential for building professional voice AI applications, live captioning services, or voice interfaces.

A comprehensive guide to implementing advanced LLM patterns using the Groq API. This workflow demonstrates how to handle chat completions, robust tool/function calling for integrating external services (like weather or search), and enforcing structured data output using JSON mode and strict schemas. Ideal for building real-time, production-grade AI features.

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.

This tool allows developers to integrate advanced AI video generation (using Kling AI) directly into continuous integration and continuous deployment pipelines (e.g., GitHub Actions, GitLab CI). Automate the creation of marketing videos, product demos, or regression video testing whenever code changes or a release occurs, ensuring consistent and timely video asset updates.

Implement comprehensive end-to-end tracing for Large Language Model (LLM) calls, chains, and complex agents using Langfuse. This workflow demonstrates techniques like OpenAI drop-in wrappers, manual span creation for RAG pipelines, tracking streamed responses, and integrating with various LLM providers (OpenAI, Anthropic). Use this to monitor latency, track token usage, and debug complex AI features in production.

A comprehensive workflow for advanced AI applications using Mistral AI. This guide covers generating text and code embeddings (1024 dimensions), implementing semantic search via cosine similarity, and building robust RAG pipelines. It also demonstrates advanced tool augmentation using function calling, allowing LLMs to interact with external APIs and databases.

This comprehensive guide provides minimal working examples for integrating with the Mistral AI API. It demonstrates core LLM functionalities including basic chat completion, streaming responses, multi-turn conversations, and generating structured JSON output. Use this guide when starting a new Mistral integration, testing API connections, or learning advanced AI patterns using TypeScript or Python.

This workflow guides users through building advanced AI voice agents using the Retell AI SDK. It demonstrates how to implement complex call flows, integrate function calling for tasks like booking appointments, and configure voice behavior for professional telephony applications.

This comprehensive guide demonstrates how to build and test a voice agent using the Retell AI SDK in TypeScript. It walks through configuring the underlying LLM with a custom persona, creating the voice agent instance, and finally initiating both outbound phone calls and web calls through the API. This is essential for integrating AI-powered voice communication into customer service platforms, call centers, or telephony automation systems.

This comprehensive guide demonstrates how to build a robust AI language tutoring session using the Speak SDK. It covers initiating conversations, submitting student responses (text or audio), performing deep pronunciation assessment at the phoneme level, and generating detailed session summaries with grammar and vocabulary metrics. Ideal for developing language education platforms and testing API integration.

A comprehensive guide demonstrating the full lifecycle of GPU resource utilization on Vast.ai. This includes searching for available hardware using CLI and REST APIs, provisioning a dedicated GPU instance (e.g., using PyTorch images), connecting via SSH, running deep learning workloads, and finally, securely destroying the instance to stop billing. Ideal for testing cloud integration or managing ML compute pipelines.

A cultural pressure‑style skill that forces the assistant to repeatedly push past failures, think systemically, validate every change, and proactively hunt down related issues until end‑to‑end ownership is demonstrated.