Skill UI

This advanced workflow provides comprehensive meeting intelligence by allowing users to search across stored transcripts using keywords and date filters. It integrates the AskFred AI assistant for natural language Q&A over single or cross-meeting data. Furthermore, it aggregates detailed meeting analytics, providing key metrics such as total duration, action items, and top keywords for robust reporting and knowledge retrieval.

Provides a robust, event-driven reference architecture for building meeting intelligence platforms using Fireflies.ai. This guide details the full production pipeline, covering multi-source meeting ingestion (Zoom, Teams), webhook processing, and subsequent data enrichment into structured action items, analytics reports, and database storage. Ideal for designing complex integrations or advanced meeting analytics systems.

This skill provides comprehensive guidelines for monitoring Granola usage, tracking team meeting patterns, and building detailed analytics dashboards. It guides users in defining critical metrics (e.g., adoption rate, efficiency score) and establishing robust data pipelines. Users can stream meeting metadata from Granola to data warehouses like BigQuery, enabling deep, customizable reporting and automated weekly insights via services like Zapier and Slack.

A comprehensive guide detailing how to implement Microsoft Defender for Cloud across various workloads (VMs, containers, SQL, storage) to achieve continuous security monitoring and compliance assessment. This process utilizes Azure Policy, Log Analytics, and Azure Arc for holistic protection, enabling adaptive remediation and meeting stringent regulatory requirements like PCI-DSS and CIS standards.

This setup provides a local development loop for integrating and testing Fathom API functionalities. It utilizes mock meeting and transcript data, making it ideal for building meeting analytics tools, simulating webhook handlers, or iterating on complex transcript processing pipelines without requiring live API calls. It ensures a robust environment for isolated development testing.

This guide provides comprehensive strategies for managing version upgrades and handling breaking changes related to the Fathom AI meeting assistant API. It includes TypeScript examples for detecting schema drift, migrating old transcript formats to new speaker-attributed segments, and implementing robust rollback mechanisms, ensuring stable integration into downstream analytics and CRM systems.

This workflow automates the extraction of key insights from meeting recordings using Fathom transcripts and summaries. It builds a robust pipeline to identify action items, sync tasks to project management tools, and analyze overall meeting patterns, streamlining post-meeting follow-up and record-keeping.