Skill UI

This skill guides the detection of unintended visual changes in user interface (UI) components. It works by capturing screenshots and performing pixel-by-pixel comparisons against approved baselines. It is essential for ensuring UI consistency across code changes, responsive breakpoints, and differing environments. Supports modern tools like Playwright, Percy, and BackstopJS.

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.

A comprehensive guide to setting up TwinMind Enterprise for large-scale corporate environments. This includes configuring on-premise deployment, integrating custom AI models (GPT-4, Claude, Gemini), and enabling Single Sign-On (SSO) using Okta. It is essential for implementing strict Role-Based Access Control (RBAC) and managing team-wide meeting intelligence and transcript sharing in a secure, enterprise setting.

Configure a comprehensive local development environment for integrating the TwinMind API. This guide walks you through setting up a project structure, configuring environment variables, and building a reusable client class using TypeScript and Axios. It is ideal for developers building applications that require local testing of transcription, summary generation, or meeting automation workflows.

This guide assists in migrating meeting AI workflows from competing tools (such as Otter.ai, Fireflies) to TwinMind. It provides a deep dive into setting up the enterprise environment, covering prerequisites, API configuration, and utilizing advanced AI models (GPT-4, Claude, Gemini) for robust transcription and intelligent summarization. Ideal for managing large-scale meeting intelligence operations.

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.

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.

Ensures database integrity via the data-validation-engine plugin by automatically enforcing data types, ranges, formats, referential integrity, and business rules whenever validation or database integrity requests arise across multi-database environments.

This skill provides robust guidelines and patterns for managing environment variables and sensitive credentials securely. It is designed to ensure that secrets, API keys, and other confidential information are never accidentally exposed or logged in Claude sessions, terminal outputs, development logs, or version control commits (like Git). Use this skill when handling sensitive data in development workflows to maintain maximum security.

This guide details how to integrate Vast.ai's GPU provisioning capabilities into automated CI/CD pipelines, such as GitHub Actions. It enables developers to run GPU-accelerated tests, model benchmarks, and validations automatically. Key features include robust instance lifecycle management (provisioning, running, and cleanup) and advanced cost-control mechanisms (e.g., setting budget limits) to ensure reliable and efficient testing in a cloud environment.

This guide demonstrates how to implement robust Role-Based Access Control (RBAC) and financial governance for cloud GPU environments. It allows organizations to allocate resources, set team-specific budgets, whitelist approved GPUs, and track spending via detailed audit logs. Ideal for multi-team research and engineering environments requiring strict cost accountability and access separation.