Skill UI

This skill provides deep integration with Google NotebookLM, enabling users to query uploaded documentation using Gemini. It ensures source-grounded, citation-backed answers, significantly reducing hallucinations. Features include managing the notebook library (add, list, search), handling authentication, and implementing a structured follow-up mechanism for comprehensive research.

This guide implements comprehensive guardrails for Perplexity Sonar API. It covers crucial aspects like query content moderation, PII sanitization, enforcing user usage quotas, selecting appropriate models based on user tiers, and scoring citation reliability to ensure safe, ethical, and cost-effective AI usage.

This skill provides production-ready patterns for integrating with the Perplexity Sonar API in both TypeScript and Python. It covers best practices for creating robust wrappers around the standard OpenAI client, including dedicated handling for citations, search results, and related questions. It also includes essential utilities like implementing exponential backoff for reliable retries. Use this when setting up complex search-augmented generation workflows or standardizing team coding practices around Perplexity usage.

This skill specializes in designing, optimizing, and evaluating prompts for Large Language Models (LLMs). It allows users to generate optimized prompt templates, structured output schemas (e.g., JSON mode, function calling), and comprehensive test suites. Use this skill when building new LLM applications, implementing complex reasoning patterns like Chain-of-Thought, defining system guardrails, or building robust evaluation frameworks to measure and improve model performance.

This comprehensive workflow guides the end-to-end implementation of Retrieval-Augmented Generation (RAG) systems. It covers critical stages, including selecting optimal embedding models, setting up vector databases, designing chunking strategies, implementing hybrid retrieval, integrating Large Language Models (LLMs), and performing rigorous evaluation. Use this when building knowledge-grounded AI applications or advanced semantic search systems.

This skill provides comprehensive patterns and best practices for integrating Retell AI into voice agents and automated phone call systems. It covers the SDK usage, common error handling (like 401 Unauthorized or 429 Rate Limited), and necessary implementation steps to ensure robust and reliable telephony automation solutions.

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.

This resource provides production-ready, structured patterns for building robust voice agent applications using the Retell AI SDK. It covers best practices such as implementing singleton client management, defining typed agent configurations with integrated LLMs, adding automatic retry logic for call failures, and managing large-scale outbound campaigns. Use this when building stable, scalable, and reliable voice automation systems.

This pattern provides established best practices for implementing efficient similarity search in production systems. It is crucial for building semantic search engines, implementing Retrieval-Augmented Generation (RAG), creating advanced recommendation systems, and optimizing retrieval performance when scaling to millions of vectors.

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.

This comprehensive guide outlines the production-grade reference architecture for advanced AI meeting systems using TwinMind. It details the end-to-end workflow, integrating high-accuracy transcription (Ear-3), AI summarization (GPT-4, Claude, Gemini), memory vault management, and automatic action item and calendar integration. Use this resource when implementing or managing a robust, automated AI meeting solution.