Skill UI

This bundle provides implementation patterns and debugging utilities for AI voice agents and telephony functions using Retell AI. It is ideal for developers integrating voice-based conversational AI into phone call systems, customer service platforms, or any telephony workflow, covering SDK usage and common error handling.

This skill provides implementation patterns for building enterprise-grade AI voice agents and automating complex phone call workflows using the Retell AI platform. It covers SDK integration, resource management, and handling common telephony APIs errors, making it ideal for developers building conversational AI applications and advanced communication systems.

This skill provides implementation patterns for managing API rate limits when integrating Retell AI for voice agents and telephony systems. It guides developers on handling common API errors (such as 429 Rate Limited) and implementing robust backoff strategies, ensuring stable and scalable operation for complex voice and communication applications.

Provides implementation patterns and reference architecture for building advanced AI voice agents and automating complex phone call workflows using the Retell AI SDK. This skill is designed for developers requiring robust voice interaction, real-time telephony integration, and comprehensive conversational AI capabilities in their applications.

This skill provides comprehensive implementation patterns and detailed steps for migrating and upgrading Retell AI integrations. It is designed for developers building advanced AI voice agents, phone call automation, and complex telephony workflows using the Retell SDK. It covers setup procedures, best practices, and standard error handling for robust deployment.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

This skill optimizes the performance of TwinMind's meeting AI operations. It fine-tunes transcription accuracy and speed using the advanced Ear-3 model, managing audio quality, and implementing caching strategies. Ideal for developers or system administrators needing to integrate, troubleshoot, or optimize high-volume meeting transcription workflows.

ZAI CLI provides comprehensive access to Z.AI's advanced capabilities, including image/video analysis, real-time web search, web page extraction, and GitHub code exploration. It is designed for users needing multi-modal content analysis, error diagnosis, and complex data processing through a unified command-line interface. Ideal for developers and analysts requiring robust, API-driven AI functionality.

A framework for building production-ready, low-latency, multimodal AI agents. It allows developers to orchestrate complex pipelines that seamlessly handle real-time audio, video, and text data. Key capabilities include integrating multiple AI services (LLMs, STT, TTS), managing conversation context, and enabling tool use and function calling for advanced, interactive AI applications.

This tool is designed to help optimize and refine prompts used with Large Language Models (LLMs). By structuring inputs, it ensures that the instructions given to AI systems are clear, comprehensive, and contextually rich, resulting in significantly higher quality and relevance of the generated output. Useful for developers, writers, and researchers.

PydanticAI is a robust Python framework that integrates Pydantic's type-checking and validation capabilities into Large Language Models (LLMs). It allows developers to build production-ready AI agents that guarantee structured, validated outputs, moving beyond simple raw strings. Key features include supported tool calling, dependency injection for testability, and multi-model compatibility across major providers like OpenAI, Anthropic, and Gemini. Use this when reliability and correct data structure are paramount.

This skill provides guidance on implementing common architectural patterns and best practices within the PyTorch framework. It is designed for developers building sophisticated deep learning models, helping to improve structure, efficiency, and code maintainability when working with neural networks.