Skill UI

Guides teams through AI SEO practices that make content discoverable and citable by assistants like ChatGPT, Gemini, Perplexity, and Google AI Overviews, including visibility audits, extractability checks, and bot access review.

Comprehensive guide for configuring Bring Your Own Key (BYOK) for major AI providers, including OpenAI, Anthropic, Google Gemini, Azure, and custom endpoints. This allows users to bypass default quotas, manage costs directly with the source provider, and utilize a wide range of specialized models within the Cursor IDE. It covers setup steps, security best practices, and cost monitoring strategies.

Uses the Gemini API to autonomously plan, search, read, and synthesize detailed research reports with streaming status updates, structured outputs, and the ability to continue previous investigations for market analysis, technical reviews, or due diligence.

Generates complete, production-ready Google Cloud Platform (GCP) code examples and infrastructure templates. It spans various modern AI patterns, including ADK agents, Genkit RAG flows, and multimodal analysis using Vertex AI and Gemini. The output includes runnable code, deployment configuration (Terraform/IaC), security best practices (IAM, VPC-SC), and monitoring setup, ensuring enterprise-grade reliability and cost optimization.

This comprehensive guide provides documentation and code examples for utilizing the Gemini API, Google's advanced multimodal AI platform. It enables developers to power applications with cutting-edge capabilities, including text generation, multimodal understanding (images, audio, video), function calling, and structured data output. Supports major languages like Python, JavaScript, and Go.

Automates Gemini operations by walking through Rube MCP/Composio tool discovery, connection management, and execution steps, ensuring schema-aware usage, memory handling, and session reuse for reliable flows.

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 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 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.

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.

Scaffolds production-ready generative AI agents on Vertex AI with Gemini models, multi-modal tools, RAG, and Cloud Run/Endpoint deployment, including observability and evaluation for enterprise scenarios.

Specializes in provisioning Vertex AI infrastructure with Terraform, covering Model Garden, Gemini endpoints, vector search indexes, ML pipelines, and enterprise-grade production deployments.