Text Automations Using Apple Shortcuts

This workflow is a multi-tool system based on AI intelligent agents, designed to obtain and analyze data from decentralized exchanges (DEX) in real-time. Users can query DEX liquidity, trading volume, trading pair quotes, and the latest transaction information, while also accessing static metadata and historical OHLCV data. It automatically calls multiple API endpoints, integrates and intelligently routes data, assisting blockchain analysts, traders, and developers in quickly obtaining detailed DEX market intelligence, thereby enhancing decision-making efficiency and market insights.

This workflow builds an intelligent chatbot using the Line Messaging API, leveraging the Llama 3 model from the Groq platform to process user messages and generate natural, fluent responses. It addresses common formatting errors and response delays encountered by traditional chatbots when handling long texts and complex messages, ensuring accurate information delivery and real-time feedback. This automated system is suitable for enterprise customer service, smart assistants, and various interactive needs, significantly enhancing user experience and operational efficiency.

This workflow is an intelligent contact management assistant that integrates the OpenAI GPT-4o model and the Airtable database. It can understand users' query intentions, automatically search for and maintain contact information, and support data addition and updates, significantly improving the efficiency and accuracy of contact management. It is suitable for customer relationship management in businesses, as well as for sales and marketing teams, helping users quickly query and maintain contact data, reduce manual operations, and enhance work efficiency.

This workflow aims to optimize project management and post-meeting task handling by automatically capturing meeting recordings and transcribing them into text. It utilizes AI for intelligent analysis to generate specific tasks, which are then recorded in an Airtable database. Additionally, it automatically sends meeting summaries and task notification emails to relevant clients and schedules follow-up meetings when necessary, effectively enhancing team collaboration efficiency and project advancement speed, ensuring that each action item is accurately captured and executed in a timely manner.

This workflow builds an intelligent chatbot that efficiently manages multiple user conversations in Telegram. Users can start, switch, and resume conversations with simple commands, while automatically generating conversation summaries and answering questions. By integrating OpenAI's intelligent language model and Google Sheets for data storage, it achieves persistent management of conversations, enhancing the user interaction experience. This solution is suitable for various scenarios, including customer service, online learning assistance, and community management.

This workflow integrates Ollama's Llama 3.2 large language model to achieve intelligent chat message processing and structured responses. After analyzing the user's natural language input, the model returns clear Q&A in JSON format, enhancing interaction efficiency. The workflow supports error handling to ensure system stability and is suitable for scenarios such as intelligent customer service, online Q&A assistants, and internal knowledge base queries, helping enterprises achieve automated and intelligent customer service.

This workflow builds an intelligent dialogue agent that utilizes OpenAI's advanced language model to process user-inputted chat messages. By combining contextual memory with external knowledge tools such as Wikipedia and SerpAPI, the agent can retrieve information in real-time and generate accurate responses. It effectively addresses the shortcomings of traditional chatbots in context management and information sourcing, making it suitable for various scenarios such as customer service automation, knowledge Q&A systems, and educational tutoring, significantly enhancing user experience and interaction intelligence.

This workflow implements batch parallel processing of multiple prompt requests through the Anthropic Claude API, automatically polling for status and retrieving results. It significantly enhances multi-tasking efficiency and simplifies the processes of request construction and response parsing. It is suitable for scenarios such as customer service systems, content generation, and data analysis. Users can easily manage multiple requests and results, and with the conversation memory feature, they can flexibly respond to complex natural language processing needs. It is an ideal solution for improving automation and efficiency.