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Zoom Rtms

OpenAIOfficialCreated by ZoomDistributed by OpenAI

  • Skill
  • Tool integration
  • Automation and integrations
  • MIT
  • No issues found
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From the author

Use when using RTMS.
— the description by Zoom

AutoGineer summary

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

The zoom-rtms skill provides the necessary protocols and architectural guidance for integrating Zoom Realtime Media Streams. It covers the WebSocket-based connection lifecycle, including signaling and media handshakes, heartbeat management, and reconnection strategies. The skill supports processing various media types such as audio, video, screen sharing, transcripts, and chat, while providing reference data for message types and event enums.

What it does

  • Establishes WebSocket connections for signaling and media streams.
  • Handles media handshake requests for audio, video, screen share, transcript, and chat data.
  • Provides reference tables for RTMS message types and event enums.
  • Defines mandatory heartbeat and reconnection logic for stream stability.
  • Supports configuration for transcript language identification.
  • Outlines the process for extracting stream IDs and generating HMAC-SHA256 signatures.

When to use it

  • When you need to ingest raw meeting or contact center media into a backend pipeline.
  • When building services for transcription, AI analysis, or recording of Zoom sessions.
  • When you need to subscribe to specific meeting events or participant media streams.
  • When you require programmatic access to media data without a visible participant bot.

Advantages

  • Provides a structured approach to managing the complex WebSocket lifecycle.
  • Includes comprehensive references for message types and event handling.
  • Offers clear guidance on signature generation and security requirements.
  • Enables granular control over media subscription and transcript settings.

Usage examples

“How do I initiate a connection to an RTMS stream?”
The agent will guide you through receiving the rtms_started webhook, generating an HMAC-SHA256 signature, and performing the signaling and media handshakes.
“What is the correct way to handle heartbeats in RTMS?”
The agent will explain that you must respond to msg_type 12 (Keep Alive Request) with msg_type 13 (Keep Alive Response) to maintain the connection.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/openai/zoom-rtms/download

  2. Unzip it into the cross-client skills folder of your project:

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

Documentation by Zoom

Zoom Realtime Media Streams

Use this skill when the integration needs real-time meeting or contact-center media as a backend stream. If the workflow needs a visible participant bot, compare against build-zoom-bot and zoom-meeting-sdk-linux before choosing RTMS.

Workflow

  1. Confirm the source surface: Meetings RTMS, Contact Center voice media, or another documented RTMS stream.

  2. Decide whether RTMS is sufficient or whether a Meeting SDK bot is required for participant identity, meeting controls, or UI-visible behavior.

  3. Implement the WebSocket lifecycle first: event subscription, connection validation, media start, heartbeat, reconnect, and shutdown.

  4. Process media types intentionally: audio, video, screen share, chat, live transcript, and metadata have different payload and timing constraints.

  5. Design downstream pipelines for buffering, transcription, AI analysis, recording, or tool invocation.

  6. Debug by isolating app setup, webhook/event delivery, stream authorization, network connectivity, and media payload decoding.

References

Files

RUNBOOK.md

RTMS 5-Minute Preflight Runbook

Use this before deep debugging. It catches the highest-frequency RTMS issues fast.

Skill Doc Standard Note

  • Agent-skill standard entrypoint is SKILL.md.

  • This runbook is an operational convention (recommended), not a required skill file.

  • SKILL.md is also a navigation convention for larger skill docs.

1) Confirm Architecture Assumption

  • RTMS is backend-first media ingestion.

  • Frontend is optional and should consume backend outputs (WebSocket/SSE/etc).

If implementation assumes frontend-only RTMS behavior, redesign first.

2) Confirm Event-Triggered Kickoff

  • Processing starts only after RTMS lifecycle start events:

    • meeting.rtms_started

    • webinar.rtms_started

    • session.rtms_started

  • Stop events should deactivate pipeline.

If media handling starts before lifecycle start, session gating is wrong.

3) Confirm Product-Specific IDs

  • Meetings/Webinars: use meeting_uuid

  • Video SDK: use session_id

  • Use rtms_stream_id from payload for stream context

Using wrong ID field commonly breaks handshake/signature.

4) Confirm Webhook Handling Pattern

  • Respond 200 immediately.

  • Do heavy work asynchronously.

  • Verify webhook signature if secret token is configured.

Slow webhook responses can trigger retries and duplicate stream attempts.

5) Confirm Connection and Heartbeat

  • Track one active connection per stream/session reference.

  • Handle heartbeat ping/pong per protocol.

  • Implement reconnection strategy explicitly.

No heartbeat handling means unexpected disconnects.

6) Confirm Media Subscription/Gating

  • Ensure requested media types match your processing path.

  • Reject/ignore media packets for inactive sessions.

  • Expose pipeline status endpoint for observability.

This avoids silent packet handling when lifecycle is not active.

7) Quick Probe Checklist

  • GET /api/health returns service alive.

  • GET /api/pipeline/status shows expected active session count.

  • Mock/media probes show:

    • media before start -> rejected

    • start event -> pipeline active

    • media after start -> accepted

Copy/Paste Validation Commands
curl -sS "$RTMS_BASE_URL/api/health"
curl -sS "$RTMS_BASE_URL/api/pipeline/status"

Expected: healthy service JSON and correct active pipeline visibility.

8) Fast Decision Tree

  • No media at all -> lifecycle event not received or wrong webhook route.

  • Duplicate streams -> delayed webhook response or no active-session guard.

  • Handshake/auth errors -> wrong credential pair or wrong session ID field.

  • Frontend appears idle -> backend bridge not connected, not an RTMS source issue.

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

No issues found by our static scan· No issues found

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/rtms · 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-rtms.zip