Skill UI

This skill manages and refines search results obtained from Exa's neural search API. It provides comprehensive control over content extraction scope, allowing users to selectively retrieve metadata, highlights, full text, or structured summaries. Key features include implementing result caching with TTL, deduplicating citations, and crucial token budget management, ensuring the retrieved context fits within LLM context window limits for robust RAG pipelines.

This workflow connects to the Fireflies.ai API via GraphQL to fetch and process detailed meeting transcripts. It handles speaker-diarized data, extracting structured insights such as action items, key topics, sentiment analysis, and speaker breakdowns. It enables the creation of comprehensive meeting intelligence reports and robust downstream pipelines. Use this when you need to automate the retrieval, summarization, or deep analysis of recorded meeting content.

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.

A comprehensive guide to diagnose and resolve common errors in Lindy AI agent workflows. Covers troubleshooting issues related to trigger failures (webhooks, email, schedule), action execution errors (Slack, Gmail, HTTP requests, code), and advanced agent step logic problems, ensuring reliable automation.

A comprehensive workflow designed for clinical professionals to search, retrieve, and synthesize medical evidence. It allows users to query clinical literature with specialized filters (e.g., specialty, evidence level), check potential drug interactions against patient profiles, and look up the latest specialty guidelines from major health organizations (like ACC/AHA, NICE). The output provides structured citations, confidence scores, and evidence grading to support informed clinical decision-making.

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.

This skill provides implementation patterns for policy guardrails when utilizing Retell AI for voice agents and phone call automation. It is designed to help ensure that AI interactions in a telephony context adhere strictly to predefined business rules and safety policies, preventing unauthorized actions or deviations from the intended conversational flow.

This guide provides a comprehensive production checklist and SDK pattern for integrating Retell AI into voice agents and telephony systems. It covers the necessary steps, error handling, and implementation patterns for deploying reliable, AI-powered phone call automation and voice interactions in a production environment.

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 an automated solution for transforming diverse, unstructured technical data sources—including documentation websites, GitHub repositories, and PDFs—into structured, actionable 'Claude AI skills.' It significantly streamlines the process of knowledge base creation, data structuring, and preparing large volumes of technical information for advanced AI applications.

This skill provides a comprehensive workflow for integrating TwinMind meeting transcription services into continuous integration/continuous deployment (CI/CD) pipelines. It automates the processes of meeting documentation, real-time transcription (using Ear-3), and structured action item extraction using advanced AI models like GPT-4 and Claude. Use this when setting up automated meeting intelligence operations or managing enterprise-level transcription workflows via API.

This guide provides comprehensive steps for deploying TwinMind integrations into production environments. It covers configuring the Chrome extension, setting up mobile app access, and managing API keys. TwinMind utilizes advanced AI models (including Ear-3 for transcription and GPT-4/Claude/Gemini for summarization) to provide real-time meeting transcription, summarization, and action item extraction. Ideal for professional teams managing high-volume AI meeting operations.