Skill UI

This comprehensive guide covers the entire data lifecycle management for Granola meeting notes. Users learn how to export data using local cache scripts or Enterprise APIs, set customized data retention policies, and handle data subject access requests (SARs). It is essential for maintaining GDPR/CCPA compliance and ensuring data portability.

This comprehensive guide details the end-to-end setup for Granola AI, an advanced AI meeting note-taking solution. It covers initial installation on macOS/Windows, authenticating with major cloud services (Google/Microsoft), and critically, granting all necessary system permissions (Screen Recording, Microphone) to ensure seamless, background audio capture and transcription. Use this guide when setting up Granola for the first time or troubleshooting connectivity issues.

This utility provides programmatic access to Granola meeting data, enabling developers to build automated post-meeting workflows. Users can read meeting notes from the local cache, integrate with MCP servers for AI context, automatically extract action items and assignees, and sync outcomes directly into coding tasks, project documentation, or issue trackers like GitHub. Ideal for automating the development lifecycle from meeting insights.

A comprehensive guide detailing the strategy for migrating from competing meeting note tools (such as Otter.ai, Fireflies, Fathom) to a new platform like Granola. This skill covers assessing migration scope, performing source-specific data exports (TXT, JSON, Markdown), selecting the optimal migration strategy (Fresh Start, Selective, Full Archive), and executing a phased transition through parallel runs and final cutover procedures. It ensures historical data preservation and minimizes operational disruption.

This guide details the comprehensive setup of Granola for large-scale, multi-departmental enterprise environments. It covers creating isolated workspaces (e.g., Engineering, HR, Sales), integrating Identity Providers using SSO/SCIM, and implementing granular compliance controls (data retention, external sharing, MFA) to ensure data security and departmental autonomy.

This comprehensive checklist guides teams through the entire process of deploying Granola in a production or enterprise environment. It covers critical phases including plan selection, security hardening (SSO, data retention), integration setup (Slack, Notion, CRM), pilot testing, and post-launch monitoring, ensuring a smooth and successful organizational rollout.

This comprehensive guide details advanced automation patterns for Granola, covering three key integration channels: Zapier workflows (connecting Granola to over 8,000 apps), the robust Enterprise API (REST access), and native integrations (e.g., Slack, Notion). Use these patterns to automate meeting note processing, extract action items, and build multi-step follow-up pipelines across your entire tech stack.

A comprehensive guide detailing common Groq API error codes (e.g., 401, 429, 400). It provides root causes, proper fixes, and code examples (Python, TypeScript) for debugging API integrations, ensuring stable operation in development environments.

A tool designed to collect comprehensive diagnostic evidence for troubleshooting issues encountered with the Groq API. It captures environment details, API connectivity status, rate limit information, latency benchmarks, and redacted application logs, making it essential for creating detailed and actionable support tickets.

Comprehensive guide for deploying applications powered by the Groq inference API. Covers best practices and specific setup instructions for multiple production environments, including Vercel Edge Functions, Google Cloud Run, Docker containers, and other containerized platforms. Essential for setting up robust, low-latency, production-ready AI services.

This guide provides comprehensive instructions for installing and configuring the official Groq SDK in both Python and TypeScript environments. It covers dependency setup, secure API key management using environment variables, and provides code examples to verify a successful connection to Groq's ultra-fast LLM inference API. Essential for initiating any project powered by Groq.

A comprehensive guide to configuring and managing Groq API access across development, staging, and production environments. This skill establishes environment-specific configurations, ensuring optimal model selection, rate limit handling, and secure secret management (using AWS, GCP, Vault) for robust and scalable LLM integrations.