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Skill
setup-zoom-webhooks
Original text · EnglishReference skill for Zoom webhooks. Use after routing to an event-driven workflow when implementing subscriptions, signature verification, delivery handling, retries, or event-type selection.
Republished in full under its license, with attribution. The source is authoritative.
name: setup-zoom-webhooks
description: Reference skill for Zoom webhooks. Use after routing to an event-driven workflow when implementing subscriptions, signature verification, delivery handling, retries, or event-type selection.
triggers:
- "zoom webhook"
- "webhook signature"
- "x-zm-signature"
- "event subscription"
- "recording completed webhook"
/setup-zoom-webhooks
Background reference for Zoom event delivery over HTTP. Prefer workflow skills first, then use this file for verification, subscription, and delivery details.
Prerequisites
Zoom app with Event Subscriptions enabled
HTTPS endpoint to receive webhooks
Webhook secret token for verification
> Need help with authentication? See the [zoom-oauth](../oauth/SKILL.md) skill for OAuth setup.
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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use after generating code, after accepting AI suggestions, or when reviewing AI-written modules. Also use when code works but feels brittle, when error handling seems thin, when orphaned resources or missing cleanup are suspected, or when the agent claims done but hidden debt may exist. Catches the specific failure patterns AI agents produce that humans would not.