Skill UI

This secondary workflow automates post-meeting follow-up processes. It intelligently extracts action items, inferring priority and assignees, and synchronizes these tasks to external project management tools (e.g., Asana, Jira, Linear). It also generates and sends follow-up emails, creating a complete, automated task lifecycle management system.

This skill is designed to help users optimize the operational costs of the TwinMind AI platform. It provides detailed usage monitoring and guidance for selecting the most appropriate subscription tier (Free, Pro, or Enterprise). Users can manage advanced features such as high-accuracy transcription (Ear-3) and AI summarization across multiple models (GPT-4, Claude, Gemini), ensuring efficient resource utilization and cost control for large-scale meeting AI operations.

This tool collects comprehensive diagnostic data for TwinMind issues. It gathers crucial information including environment details (OS, Node.js version, memory), API health status, network connectivity, and configuration snapshots. It is essential for generating detailed bug reports or preparing for technical support requests, helping developers quickly pinpoint the root cause of complex problems.

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.

This skill guides users through transcribing live meetings (Zoom, Meet, Teams) or voice memos using advanced AI. It provides full transcriptions with timestamps, and advanced AI processing to automatically generate structured meeting summaries, prioritized action items with assignees, and key discussion points. It is essential for meeting accountability, knowledge capture, and project follow-up.

A comprehensive runbook for managing and resolving common incidents within the TwinMind meeting AI platform. This guide covers failures such as transcription not starting, audio capture issues, syncing errors, and calendar disconnections. It provides API setup, code snippets, and detailed troubleshooting steps to ensure a stable, continuous meeting AI workflow.

Configure the TwinMind AI platform across development, staging, and production environments. This setup ensures secure separation of accounts and manages API keys for robust meeting AI operations, supporting enterprise features like on-premise deployment and SSO integration.

Provides comprehensive observability for the TwinMind AI meeting system. This skill allows users to monitor critical performance metrics, including transcription quality (using Ear-3 model), meeting coverage, action item extraction accuracy, and memory vault health. It is essential for implementing robust, enterprise-grade AI workflows and managing the operational status of advanced meeting AI applications.

A comprehensive checklist designed to ensure TwinMind integrations are deployed securely, stably, and compliantly in a production environment. It covers critical best practices across multiple domains, including authentication, data encryption (PII, GDPR), robust infrastructure setup (auto-scaling, load balancing), advanced monitoring (metrics, tracing, alerting), and full CI/CD deployment workflows. Use this when preparing for go-live or conducting production audits.

A comprehensive utility suite for handling external API rate limits, specifically designed for services like TwinMind. It implements advanced patterns including exponential backoff with jitter and request queue management. Use this to ensure resilient, high-throughput, and stable interactions with resource-constrained AI APIs, minimizing 429 errors.

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.

This guide provides production-ready patterns for implementing the TwinMind AI memory and meeting intelligence REST API. It covers best practices for authentication, storing contextual memories, searching knowledge bases, and processing meeting transcripts to extract actionable insights. Use this when building robust, scalable AI-powered applications or standardizing team coding practices.