Zoom RTMS Meeting Assistant

Headless capture service for Zoom meetings using Real-Time Media Streams (RTMS). Receives webhook events, connects to RTMS WebSockets, records all media, and runs AI analysis via OpenClaw.

Webhook Events Handled

This skill processes two Zoom webhook events:

• meeting.rtms_started — Zoom sends this when RTMS is activated for a meeting. Contains server_urls, rtms_stream_id, and meeting_uuid needed to connect to the RTMS WebSocket.
• meeting.rtms_stopped — Zoom sends this when RTMS ends (meeting ended or RTMS disabled). Triggers cleanup: closes WebSocket connections, generates screenshare PDF, sends summary notification.

Webhook Dependency

This skill needs a public webhook endpoint to receive these events from Zoom.

Preferred: Use the ngrok-unofficial-webhook-skill (skills/ngrok-unofficial-webhook-skill). It auto-discovers this skill via webhookEvents in skill.json, notifies the user, and offers to route events here.

Other webhook solutions (e.g. custom servers, cloud functions) will work but require additional integration to forward payloads to this service.

Prerequisites

cd skills/zoom-meeting-assistance-rtms-unofficial-community
npm install

Requires ffmpeg for post-meeting media conversion.

Environment Variables

Set these in the skill's .env file:

Required:

• ZOOM_SECRET_TOKEN — Zoom webhook secret token


• ZOOM_CLIENT_ID — Zoom app Client ID


• ZOOM_CLIENT_SECRET — Zoom app Client Secret

Optional:

• PORT — Server port (default: 3000)


• AI_PROCESSING_INTERVAL_MS — AI analysis frequency in ms (default: 30000)


• AI_FUNCTION_STAGGER_MS — Delay between AI calls in ms (default: 5000)


• AUDIO_DATA_OPT — 1 = mixed stream, 2 = multi-stream (default: 2)


• OPENCLAW_NOTIFY_CHANNEL — Notification channel (default: whatsapp)


• OPENCLAW_NOTIFY_TARGET — Phone number / target for notifications

Starting the Service

cd skills/zoom-meeting-assistance-rtms-unofficial-community
node index.js

This starts an Express server listening for Zoom webhook events on PORT.

⚠️ Important: Before forwarding webhooks to this service, always check if it's running:

# Check if service is listening on port 3000
lsof -i :3000

If nothing is returned, start the service first before forwarding any webhook events.

Typical flow:

Start the server as a background process

Zoom sends meeting.rtms_started webhook → service connects to RTMS WebSocket

Media streams in real-time: audio, video, transcript, screenshare, chat

AI processing runs periodically (dialog suggestions, sentiment, summary)

meeting.rtms_stopped → service closes connections, generates screenshare PDF

Recorded Data

All recordings are stored organized by date:

skills/zoom-meeting-assistance-rtms-unofficial-community/recordings/YYYY/MM/DD/{streamId}/

Each stream folder contains:

File

Content

Searchable

metadata.json	Meeting metadata (UUID, stream ID, operator, start time)	✅
transcript.txt	Plain text transcript with timestamps and speaker names	✅ Best for searching — grep-friendly, one line per utterance
transcript.vtt	VTT format transcript with timing cues	✅
transcript.srt	SRT format transcript	✅
events.log	Participant join/leave, active speaker changes (JSON lines)	✅
chat.txt	Chat messages with timestamps	✅
ai_summary.md	AI-generated meeting summary (markdown)	✅ Key document — read this first for meeting overview
ai_dialog.json	AI dialog suggestions	✅
ai_sentiment.json	Sentiment analysis per participant	✅
mixedaudio.raw	Mixed audio stream (raw PCM)	❌ Binary
activespeakervideo.h264	Active speaker video (raw H.264)	❌ Binary
processed/screenshare.pdf	Deduplicated screenshare frames as PDF	❌ Binary

All summaries are also copied to a central folder for easy access:

skills/zoom-meeting-assistance-rtms-unofficial-community/summaries/summary_YYYY-MM-DDTHH-MM-SS_{streamId}.md

Searching & Querying Past Meetings

To find and review past meeting data:

# List all recorded meetings by date
ls -R recordings/

# List meetings for a specific date
ls recordings/2026/01/28/

# Search across all transcripts for a keyword
grep -rl "keyword" recordings/*/*/*/*/transcript.txt

# Search for what a specific person said
grep "Chun Siong Tan" recordings/*/*/*/*/transcript.txt

# Read a meeting summary
cat recordings/YYYY/MM/DD/<streamId>/ai_summary.md

# Search summaries for a topic
grep -rl "topic" recordings/*/*/*/*/ai_summary.md

# Check who attended a meeting
cat recordings/YYYY/MM/DD/<streamId>/events.log

# Get sentiment for a meeting
cat recordings/YYYY/MM/DD/<streamId>/ai_sentiment.json

The .txt, .md, .json, and .log files are all text-based and searchable. Start with ai_summary.md for a quick overview, then drill into transcript.txt for specific quotes or details.

API Endpoints

# Toggle WhatsApp notifications on/off
curl -X POST  -H "Content-Type: application/json" -d '{"enabled": false}'

# Check notification status
curl

Post-Meeting Processing

When meeting.rtms_stopped fires, the service automatically:

Generates PDF from screenshare images

Converts mixedaudio.raw → mixedaudio.wav

Converts activespeakervideo.h264 → activespeakervideo.mp4

Muxes mixed audio + active speaker video into final_output.mp4

Manual conversion scripts are available but note that auto-conversion runs on meeting end, so manual re-runs are rarely needed.

Reading Meeting Data

After or during a meeting, read files from recordings/YYYY/MM/DD/{streamId}/:

# List recorded meetings by date
ls -R recordings/

# Read transcript
cat recordings/YYYY/MM/DD/<streamId>/transcript.txt

# Read AI summary
cat recordings/YYYY/MM/DD/<streamId>/ai_summary.md

# Read sentiment analysis
cat recordings/YYYY/MM/DD/<streamId>/ai_sentiment.json

Prompt Customization

Want different summary styles or analysis? Customize the AI prompts to fit your needs!

Edit these files to change AI behavior:

File

Purpose

Example Customizations

summary_prompt.md	Meeting summary generation	Bullet points vs prose, focus areas, length
query_prompt.md	Query response formatting	Response style, detail level
query_prompt_current_meeting.md	Real-time meeting analysis	What to highlight during meetings
query_prompt_dialog_suggestions.md	Dialog suggestion style	Formal vs casual, suggestion count
query_prompt_sentiment_analysis.md	Sentiment scoring logic	Custom sentiment categories, thresholds

Tip: Back up the originals before editing, so you can revert if needed.