Skill UI

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.

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.

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 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.

A comprehensive reference guide for developers to debug and resolve common errors encountered when integrating with the Gamma API. Covers specific error codes such as authentication failures (401/403), rate limit issues (429), content generation failures, and timeouts. Provides detailed solutions, best practices, and code snippets for robust error handling and reliable API usage.

A comprehensive runbook providing systematic, step-by-step procedures for responding to and resolving critical incidents related to Gamma integration. It covers diagnostics for full outages, 5xx errors, rate limit exceedances, and high latency, ensuring rapid and structured incident handling during production failures.

This guide provides comprehensive techniques for managing API rate limits and ensuring reliable, high-volume API usage with Gamma. It covers methods such as reading rate limit headers, implementing exponential backoff for automatic retries, and building robust request queuing systems to handle throttling gracefully.

This guide demonstrates how to build a resilient, event-driven architecture for Gamma API integrations when native webhooks are unavailable. It utilizes a poll-based pattern, creating synthetic application-level events (start, completed, failed) by wrapping polling logic within an EventEmitter or message queue. Ideal for building background workers that reliably track and react to the full lifecycle of content generation.

Generates conventional commit messages using AI by analyzing staged Git changes and recommending standardized text for features or fixes, helping teams keep commit history consistent during development.

Generates production-ready Docker Compose configurations for multi-container apps by defining services, networks, volumes, health checks, and resource limits based on user architecture input.

Automatically generates conventional commit messages by analyzing staged Git changes, identifying types and breaking changes, and formatting the result so you can approve standardized commits quickly after running `/commit-smart` or `/gc`.