Generated banner for Zoom General
Generated logo for Zoom General

Zoom General

OpenAIOfficialCreated by ZoomDistributed by OpenAI

  • Skill
  • Tool integration
  • Automation and integrations
  • MIT
  • Medium risk

From the author

Use when comparing products.
— the description by Zoom

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Zoom General serves as a strategic starting point for developers and architects to navigate the Zoom ecosystem. It provides a structured workflow to classify integration requirements, identify the correct Zoom product surface (such as APIs, SDKs, or webhooks), and determine the necessary authentication model. By guiding users through a decision-making process, it ensures that developers select the right tool for their specific use case before routing to more specialized skills.

What it does

  • Classifies integration jobs based on desired outcomes like embedding meetings or automating resources.
  • Maps requirements to specific Zoom surfaces including REST APIs, WebSockets, and various SDKs.
  • Provides guidance on selecting the correct authentication and scope model for an integration.
  • Facilitates routing to specialized skills once the product context is established.
  • Offers comparative context for tasks that span multiple Zoom product boundaries.

When to use it

  • When you are in the early stages of planning a Zoom integration and need to choose the right app type.
  • When you need to compare different Zoom surfaces to determine which fits your technical requirements.
  • When a task involves multiple Zoom products and requires a high-level architectural approach.
  • When you need to confirm the correct authentication flow for a specific integration scenario.

Advantages

  • Prevents premature selection of tools by enforcing a structured classification process.
  • Clarifies the distinction between user-level OAuth, account-level OAuth, and SDK signatures.
  • Reduces complexity by routing users to narrow, specialized skills after initial planning.
  • Provides a clear decision matrix for choosing between General Apps, Server-to-Server OAuth, and Webhook Only apps.

Usage examples

“I want to build an app that automates meeting reports. Which Zoom app type should I use?”
The agent will guide you to classify the job as backend automation, leading to the recommendation of Server-to-Server OAuth for its lack of user interaction requirements.
“Should I use the Meeting SDK or the Video SDK for my project?”
The agent will help you compare the two based on your specific embedding needs and surface requirements, ensuring you choose the correct SDK for your use case.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/openai/zoom-general/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip zoom-general.zip -d .agents/skills

Documentation by Zoom

Zoom General

Use this skill for cross-product platform context after the user’s goal is understood. For first-step routing, prefer start, plan-zoom-product, or plan-zoom-integration.

Workflow

  1. Classify the job by outcome: embed meetings, custom video, automate resources, consume events, process media, use meeting intelligence, or publish a Marketplace app.

  2. Choose the Zoom surface that owns the behavior: REST API, webhooks, WebSockets, Meeting SDK, Video SDK, Zoom Apps SDK, Phone, Contact Center, Virtual Agent, Scribe, Rivet, or Cobrowse.

  3. Confirm auth and scope model before implementation; Zoom surfaces differ on user-level OAuth, account-level OAuth, and SDK signatures.

  4. Route to the narrow skill once the surface is chosen rather than keeping broad guidance in context.

  5. Use the preserved guide only when a task crosses product boundaries or needs detailed comparison tables.

References

Files

use-cases/team-chat-llm-bot.md

Use Case: AI-Powered Team Chat Bot with LLM Integration

Build an intelligent Zoom Team Chat bot that uses an LLM provider for natural language understanding and can be chained with other Zoom skills.

Scenario

Create a chatbot that:

  1. Responds to natural language queries

  2. Can trigger Zoom Meeting creation

  3. Can search and retrieve chat history

  4. Provides intelligent assistance across Zoom products

Skills Required

  • zoom-team-chat - Primary skill for chatbot functionality

  • zoom-rest-api - For meeting creation, user management

  • oauth - For user authentication flows (optional)

  • zoom-meeting-sdk - For advanced meeting integrations (optional)

Architecture

User sends message → Team Chat Bot receives webhook
                            ↓
                     Call LLM provider
                            ↓
                     Parse LLM response for intent
                            ↓
         ┌──────────────────┼──────────────────┐
         │                  │                  │
    Create Meeting    Get User Info    Send Response
    (REST API)        (REST API)       (Team Chat)

Implementation Steps

1. Setup Team Chat Bot

Skill: zoom-team-chat

// Handle bot notification
case 'bot_notification': {
  const { toJid, cmd, accountId } = payload;

  // Call LLM
  const llmResponse = await callLLM(cmd);

  // Check for intents
  const intent = parseIntent(llmResponse);

  if (intent.type === 'create_meeting') {
    await handleCreateMeeting(toJid, accountId, intent);
  } else {
    await sendTextMessage(toJid, accountId, llmResponse);
  }
}
2. Create Meeting Intent

Skills: zoom-team-chat + zoom-rest-api

async function handleCreateMeeting(toJid, accountId, intent) {
  // Extract meeting details from LLM response
  const { topic, start_time, duration } = intent.details;

  // Create meeting using REST API
  const meeting = await fetch('https://api.zoom.us/v2/users/me/meetings', {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${userAccessToken}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      topic,
      type: 2, // Scheduled meeting
      start_time,
      duration
    })
  });

  const meetingData = await meeting.json();

  // Send meeting details back to Team Chat
  await sendChatbotMessage(toJid, accountId, {
    head: { text: 'Meeting Created' },
    body: [
      { type: 'message', text: `Meeting "${topic}" created successfully!` },
      {
        type: 'fields',
        items: [
          { key: 'Start Time', value: start_time },
          { key: 'Duration', value: `${duration} minutes` },
          { key: 'Join URL', value: meetingData.join_url }
        ]
      },
      {
        type: 'actions',
        items: [
          { text: 'Join Meeting', value: `join_${meetingData.id}`, style: 'Primary' },
          { text: 'Share Link', value: `share_${meetingData.id}`, style: 'Default' }
        ]
      }
    ]
  });
}
3. LLM Integration with Intent Parsing

Skill: zoom-team-chat

const llmClient = createLLMClient({
  apiKey: process.env.LLM_API_KEY,
  model: process.env.LLM_MODEL
});

async function callLLM(userMessage) {
  const response = await llmClient.generate({
    max_tokens: 1024,
    system: `You are a Zoom assistant bot. You can help users with:
- Creating meetings
- Finding user information
- Answering questions about Zoom
- General assistance

When a user wants to create a meeting, respond with JSON:
{
  "intent": "create_meeting",
  "details": {
    "topic": "Meeting topic",
    "start_time": "ISO 8601 format",
    "duration": 60
  }
}

Otherwise, provide a helpful text response.`,
    messages: [{ role: 'user', content: userMessage }]
  });

  return response.content[0].text;
}

function parseIntent(llmResponse) {
  try {
    // Check if response is JSON
    if (llmResponse.trim().startsWith('{')) {
      const intent = JSON.parse(llmResponse);
      return intent;
    }
  } catch (e) {
    // Not JSON, regular text response
  }

  return { type: 'text_response', message: llmResponse };
}

Skill Chaining Examples

Example 1: Create Meeting from Chat

User: /bot schedule a team standup tomorrow at 10am for 30 minutes

Flow:

  1. zoom-team-chat: Receives command via webhook

  2. LLM parses: "create_meeting" intent

  3. zoom-rest-api: Creates meeting

  4. zoom-team-chat: Sends confirmation with buttons

Example 2: Find User and Start DM

User: /bot who is John Doe?

Flow:

  1. zoom-team-chat: Receives query

  2. LLM identifies: "find_user" intent

  3. zoom-rest-api: Searches users

  4. zoom-team-chat: Shows user info with "Send DM" button

Example 3: Search Chat History

User: /bot find messages about project alpha

Flow:

  1. zoom-team-chat: Receives search query

  2. zoom-rest-api: Searches chat messages

  3. zoom-team-chat: Displays results with links

Environment Variables

# Team Chat (from zoom-team-chat skill)
ZOOM_CLIENT_ID=
ZOOM_CLIENT_SECRET=
ZOOM_BOT_JID=
ZOOM_VERIFICATION_TOKEN=
ZOOM_ACCOUNT_ID=

# LLM Integration
LLM_API_KEY=
LLM_MODEL=

# Server
PORT=4000

Advanced: Multi-Skill Integration

With webhooks

Subscribe to meeting events and notify in Team Chat:

// Webhook handler for meeting events
app.post('/meeting-webhook', (req, res) => {
  const { event, payload } = req.body;

  if (event === 'meeting.started') {
    // Notify in Team Chat
    await sendChatbotMessage(channelJid, accountId, {
      body: [
        { type: 'message', text: `Meeting "${payload.object.topic}" has started!` },
        {
          type: 'actions',
          items: [
            { text: 'Join Now', value: `join_${payload.object.id}`, style: 'Primary' }
          ]
        }
      ]
    });
  }

  res.status(200).send();
});

Testing Checklist

  • [ ] Bot responds to natural language queries

  • [ ] Can create meetings from chat commands

  • [ ] Meeting details sent back to Team Chat

  • [ ] Buttons trigger appropriate actions

  • [ ] LLM intent parsing works correctly

  • [ ] Error handling for failed API calls

  • [ ] Multi-turn conversation support

Resources

  • zoom-team-chat skill

  • zoom-rest-api skill

  • oauth skill

  • Chatbot Setup Example

  • LLM Integration Example

Next Steps

  1. Build basic chatbot using Chatbot Setup

  2. Add LLM integration

  3. Implement intent parsing

  4. Add REST API calls for meetings

  5. Test end-to-end flow

  6. Deploy to production

Requirements and compatibility

Written for: OpenAI Codex

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

What our scanner noticed· Medium risk

  • Possible data-exfiltration endpoints
  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Zoom
Publisher
OpenAI (Official)
Distributed by
OpenAI
Source
openai/plugins · plugins/zoom/skills/general · commit d420d13
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from plugins/zoom/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Zoom or OpenAI.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download zoom-general.zip