Skill UI

This workflow connects to the Fireflies.ai API via GraphQL to fetch and process detailed meeting transcripts. It handles speaker-diarized data, extracting structured insights such as action items, key topics, sentiment analysis, and speaker breakdowns. It enables the creation of comprehensive meeting intelligence reports and robust downstream pipelines. Use this when you need to automate the retrieval, summarization, or deep analysis of recorded meeting content.

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 minimal working examples and comprehensive instructions for querying the Fireflies.ai GraphQL API. Users can learn how to list workspace users, fetch recent meeting transcripts, and retrieve detailed summaries, including action items and keywords, using code examples in TypeScript, Python, and Bash. Ideal for initial API setup, integration testing, or learning GraphQL patterns for meeting data.

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 script provides a complete solution for setting up a webhook receiver for Fireflies.ai. It includes crucial HMAC-SHA256 signature verification to ensure data integrity, and then uses the Fireflies API to fetch the full transcript details, summary, and action items. Ideal for building real-time meeting intelligence pipelines, integrating meeting data into CRMs, or automating task creation.

Automates the conversion of meeting notes from Granola into structured development artifacts. This pipeline uses Zapier and GitHub Actions to automatically extract action items and decisions, creating corresponding issues in GitHub, tasks in Linear, and summarizing updates in Slack. It ensures that meeting outcomes are immediately tracked and integrated into the development workflow.

This comprehensive workflow automates the entire post-meeting cycle. It enhances raw transcripts by structuring notes, extracting action items, and summarizing key decisions using advanced AI models. Users can then seamlessly share these structured notes to integrations like Slack, Notion, and CRMs (HubSpot), while also using the Chat feature to draft follow-up emails and automatically update contact records in the built-in CRM.

This skill outlines the complete workflow for capturing and structuring meeting notes using Granola. It captures audio, transcribes the content, and intelligently merges live user notes with the full transcript. The output is structured into key sections like Summaries, Decisions, and Action Items. Users can also interact with Granola Chat for post-meeting Q&A, making it an essential tool for professional meeting documentation and follow-up.

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.

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.

This comprehensive guide teaches the best practices for writing effective, blameless postmortem reports following technical incidents. It provides a structured approach to documenting timelines, conducting root cause analysis (RCA), identifying systemic failures, and creating actionable follow-up items. Mastering this skill is crucial for fostering organizational learning and preventing future service disruptions.

Offers a decision framework that starts with regex for structured text extraction, flags low-confidence items, and only triggers a cheap LLM validator for edge cases to balance cost and accuracy.