Skill UI

This guide provides comprehensive setup for implementing CI/CD pipelines for Fireflies.ai integrations. It covers best practices for validating GraphQL queries, running mock-based unit tests (using Vitest), and executing live integration tests against the actual API. It integrates seamlessly with GitHub Actions, ensuring that code changes are automatically validated in a stable, automated testing environment before deployment. Essential for maintaining API stability and robustness.

This comprehensive guide serves as a quick reference for diagnosing and fixing common errors encountered when interacting with the Fireflies.ai GraphQL API. It covers specific error codes (e.g., authentication failure, rate limits, invalid parameters) and provides actionable fixes, diagnostic scripts, and schema introspection techniques, ensuring seamless API integration and rapid troubleshooting.

This utility collects comprehensive diagnostic evidence for Fireflies.ai integration issues. It captures environment details (Node/Python versions, OS), checks API connectivity, gathers account metadata, and pulls recent transcript metadata. The output is a safe, redacted bundle ideal for submitting support tickets or troubleshooting persistent platform problems.

A comprehensive guide for deploying Fireflies.ai integrations into production environments. This skill covers setting up the GraphQL client, implementing secure webhook receivers with signature verification, and managing multi-platform deployments across Vercel, Docker, and Google Cloud Run. Ideal for integrating AI-powered meetings data into production services.

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.

This guide details the comprehensive setup process for authenticating with the Fireflies.ai GraphQL API. It covers retrieving the API key, configuring environment variables, and verifying connectivity using code examples for Node.js, Python, and cURL. Essential for securely integrating Fireflies data into external applications and automating transcript access.

This guide details a complete local development workflow for integrating with Fireflies.ai GraphQL. It outlines setting up the project structure, recording real API responses as mock fixtures, creating a custom mock client, and writing robust unit tests. Use this methodology to ensure fast, reliable, and isolated development cycles without dependency on live APIs or worrying about rate limits.

This guide provides a comprehensive, multi-step technical process for migrating historical meeting recordings and data into the Fireflies.ai ecosystem. It covers various scenarios, including batch uploads from public URLs, importing authenticated files (S3/GCS), direct file uploads, and tracking progress via webhooks, ensuring a robust and validated transition from competitor platforms.

This comprehensive guide details how to configure Fireflies.ai across multiple isolated environments (Development, Staging, and Production). It emphasizes best practices for managing environment-specific secrets, API keys, and webhook URLs. By implementing robust environment separation, you can prevent data leakage and ensure that changes in one environment do not impact the stability or data integrity of others, which is crucial for CI/CD pipelines.

A comprehensive toolkit for monitoring the health and performance of Fireflies.ai integration. It tracks crucial metrics such as API connectivity, webhook delivery status, transcript processing latency, and seat utilization. Built using Prometheus metrics and suitable for Grafana dashboards, ensuring reliable data pipeline monitoring for engineering teams.

A comprehensive guide detailing best practices for optimizing GraphQL queries against external APIs, demonstrated using Fireflies.ai. Learn advanced techniques such as requesting minimum required fields (field selection), implementing robust caching strategies (LRU/Redis), and managing rate limits through controlled batch processing and pagination. This ensures stable, fast, and scalable data retrieval.

A comprehensive guide and implementation for managing rate limits, exponential backoff, and request queuing when interacting with the Fireflies.ai GraphQL API. This skill ensures robust API calling by detecting 429 errors, handling per-plan quotas, and implementing reliable retry mechanisms.