Skill UI

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.

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 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.

This guide outlines essential security protocols for integrating Fireflies.ai. It covers best practices for managing sensitive API keys (e.g., environment variables, pre-commit hooks), implementing robust HMAC-SHA256 webhook signature verification in multiple languages (TypeScript, Python), and correctly configuring transcript privacy levels to ensure data confidentiality. Ideal for developers setting up production-ready integrations.

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.

This skill provides a structured methodology for reviewing code commits intended to fix security vulnerabilities or address audit findings. It ensures that the original root cause is properly resolved, verifies the fix hasn't introduced new bugs or vulnerabilities (regressions), and validates the overall completeness of the remediation effort. Ideal for security-critical code reviews.

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