[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PXWQMFZGE4ENWHC1F54XJC":3,"skill-same:01M6PXWQMFZGE4ENWHC1F54XJC":155},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":27,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":57,"repositoryHomepageUrl":6,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":59,"updatedAt":60,"install":61,"resources":74},"01M6PXWQMFZGE4ENWHC1F54XJC","zoom-meeting-assistance-rtms-unofficial-community",null,"Zoom RTMS Meeting Assistant — start on-demand to capture meeting audio, video, transcript, screenshare, and chat via Zoom Real-Time Media Streams. Handles meeting.rtms_started and meeting.rtms_stopped webhook events. Provides AI-powered dialog suggestions, sentiment analysis, and live summaries with WhatsApp notifications. Use when a Zoom RTMS webhook fires or the user asks to record\u002Fanalyze a meeting.","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F媒体与流媒体\u002Fzoom-meeting-assistance-with-rtms-unofficial-community-skill\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":25,"taxonomyTypeDesc":26},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"community","zoom meeting assistance rtms unofficial community","rtms","meeting","zoom","Media","Audio","analyze","dialog","use","transcript","chat","time","webhook","Video","with","streams","analysis","real-time","notifications","2026-11-10 10:12:30",false,"---\nname: zoom-meeting-assistance-rtms-unofficial-community\ndescription: Zoom RTMS Meeting Assistant — start on-demand to capture meeting audio, video, transcript, screenshare, and chat via Zoom Real-Time Media Streams. Handles meeting.rtms_started and meeting.rtms_stopped webhook events. Provides AI-powered dialog suggestions, sentiment analysis, and live summaries with WhatsApp notifications. Use when a Zoom RTMS webhook fires or the user asks to record\u002Fanalyze a meeting.\n---\n\n# Zoom RTMS Meeting Assistant\n\nHeadless 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.\n\n## Webhook Events Handled\n\nThis skill processes two Zoom webhook events:\n\n- **`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.\n- **`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.\n\n## Webhook Dependency\n\nThis skill needs a public webhook endpoint to receive these events from Zoom.\n\n**Preferred:** Use the **ngrok-unofficial-webhook-skill** (`skills\u002Fngrok-unofficial-webhook-skill`). It auto-discovers this skill via `webhookEvents` in `skill.json`, notifies the user, and offers to route events here.\n\nOther webhook solutions (e.g. custom servers, cloud functions) will work but require additional integration to forward payloads to this service.\n\n## Prerequisites\n\n```bash\ncd skills\u002Fzoom-meeting-assistance-rtms-unofficial-community\nnpm install\n```\n\nRequires `ffmpeg` for post-meeting media conversion.\n\n## Environment Variables\n\nSet these in the skill's `.env` file:\n\n**Required:**\n- `ZOOM_SECRET_TOKEN` — Zoom webhook secret token\n- `ZOOM_CLIENT_ID` — Zoom app Client ID\n- `ZOOM_CLIENT_SECRET` — Zoom app Client Secret\n\n**Optional:**\n- `PORT` — Server port (default: `3000`)\n- `AI_PROCESSING_INTERVAL_MS` — AI analysis frequency in ms (default: `30000`)\n- `AI_FUNCTION_STAGGER_MS` — Delay between AI calls in ms (default: `5000`)\n- `AUDIO_DATA_OPT` — `1` = mixed stream, `2` = multi-stream (default: `2`)\n- `OPENCLAW_NOTIFY_CHANNEL` — Notification channel (default: `whatsapp`)\n- `OPENCLAW_NOTIFY_TARGET` — Phone number \u002F target for notifications\n\n## Starting the Service\n\n```bash\ncd skills\u002Fzoom-meeting-assistance-rtms-unofficial-community\nnode index.js\n```\n\nThis starts an Express server listening for Zoom webhook events on `PORT`.\n\n**⚠️ Important:** Before forwarding webhooks to this service, always check if it's running:\n\n```bash\n# Check if service is listening on port 3000\nlsof -i :3000\n```\n\nIf nothing is returned, start the service first before forwarding any webhook events.\n\n**Typical flow:**\n1. Start the server as a background process\n2. Zoom sends `meeting.rtms_started` webhook → service connects to RTMS WebSocket\n3. Media streams in real-time: audio, video, transcript, screenshare, chat\n4. AI processing runs periodically (dialog suggestions, sentiment, summary)\n5. `meeting.rtms_stopped` → service closes connections, generates screenshare PDF\n\n## Recorded Data\n\nAll recordings are stored organized by date:\n```\nskills\u002Fzoom-meeting-assistance-rtms-unofficial-community\u002Frecordings\u002FYYYY\u002FMM\u002FDD\u002F{streamId}\u002F\n```\n\nEach stream folder contains:\n\n| File | Content | Searchable |\n|------|---------|-----------|\n| `metadata.json` | Meeting metadata (UUID, stream ID, operator, start time) | ✅ |\n| `transcript.txt` | Plain text transcript with timestamps and speaker names | ✅ Best for searching — grep-friendly, one line per utterance |\n| `transcript.vtt` | VTT format transcript with timing cues | ✅ |\n| `transcript.srt` | SRT format transcript | ✅ |\n| `events.log` | Participant join\u002Fleave, active speaker changes (JSON lines) | ✅ |\n| `chat.txt` | Chat messages with timestamps | ✅ |\n| `ai_summary.md` | AI-generated meeting summary (markdown) | ✅ Key document — read this first for meeting overview |\n| `ai_dialog.json` | AI dialog suggestions | ✅ |\n| `ai_sentiment.json` | Sentiment analysis per participant | ✅ |\n| `mixedaudio.raw` | Mixed audio stream (raw PCM) | ❌ Binary |\n| `activespeakervideo.h264` | Active speaker video (raw H.264) | ❌ Binary |\n| `processed\u002Fscreenshare.pdf` | Deduplicated screenshare frames as PDF | ❌ Binary |\n\nAll summaries are also copied to a central folder for easy access:\n```\nskills\u002Fzoom-meeting-assistance-rtms-unofficial-community\u002Fsummaries\u002Fsummary_YYYY-MM-DDTHH-MM-SS_{streamId}.md\n```\n\n## Searching & Querying Past Meetings\n\nTo find and review past meeting data:\n\n```bash\n# List all recorded meetings by date\nls -R recordings\u002F\n\n# List meetings for a specific date\nls recordings\u002F2026\u002F01\u002F28\u002F\n\n# Search across all transcripts for a keyword\ngrep -rl \"keyword\" recordings\u002F*\u002F*\u002F*\u002F*\u002Ftranscript.txt\n\n# Search for what a specific person said\ngrep \"Chun Siong Tan\" recordings\u002F*\u002F*\u002F*\u002F*\u002Ftranscript.txt\n\n# Read a meeting summary\ncat recordings\u002FYYYY\u002FMM\u002FDD\u002F\u003CstreamId>\u002Fai_summary.md\n\n# Search summaries for a topic\ngrep -rl \"topic\" recordings\u002F*\u002F*\u002F*\u002F*\u002Fai_summary.md\n\n# Check who attended a meeting\ncat recordings\u002FYYYY\u002FMM\u002FDD\u002F\u003CstreamId>\u002Fevents.log\n\n# Get sentiment for a meeting\ncat recordings\u002FYYYY\u002FMM\u002FDD\u002F\u003CstreamId>\u002Fai_sentiment.json\n```\n\nThe `.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.\n\n## API Endpoints\n\n```bash\n# Toggle WhatsApp notifications on\u002Foff\ncurl -X POST http:\u002F\u002Flocalhost:3000\u002Fapi\u002Fnotify-toggle -H \"Content-Type: application\u002Fjson\" -d '{\"enabled\": false}'\n\n# Check notification status\ncurl http:\u002F\u002Flocalhost:3000\u002Fapi\u002Fnotify-toggle\n```\n\n## Post-Meeting Processing\n\nWhen `meeting.rtms_stopped` fires, the service automatically:\n1. Generates PDF from screenshare images\n2. Converts `mixedaudio.raw` → `mixedaudio.wav`\n3. Converts `activespeakervideo.h264` → `activespeakervideo.mp4`\n4. Muxes mixed audio + active speaker video into `final_output.mp4`\n\nManual conversion scripts are available but note that auto-conversion runs on meeting end, so manual re-runs are rarely needed.\n\n## Reading Meeting Data\n\nAfter or during a meeting, read files from `recordings\u002FYYYY\u002FMM\u002FDD\u002F{streamId}\u002F`:\n\n```bash\n# List recorded meetings by date\nls -R recordings\u002F\n\n# Read transcript\ncat recordings\u002FYYYY\u002FMM\u002FDD\u002F\u003CstreamId>\u002Ftranscript.txt\n\n# Read AI summary\ncat recordings\u002FYYYY\u002FMM\u002FDD\u002F\u003CstreamId>\u002Fai_summary.md\n\n# Read sentiment analysis\ncat recordings\u002FYYYY\u002FMM\u002FDD\u002F\u003CstreamId>\u002Fai_sentiment.json\n```\n\n## Prompt Customization\n\nWant different summary styles or analysis? Customize the AI prompts to fit your needs!\n\nEdit these files to change AI behavior:\n\n| File | Purpose | Example Customizations |\n|------|---------|----------------------|\n| `summary_prompt.md` | Meeting summary generation | Bullet points vs prose, focus areas, length |\n| `query_prompt.md` | Query response formatting | Response style, detail level |\n| `query_prompt_current_meeting.md` | Real-time meeting analysis | What to highlight during meetings |\n| `query_prompt_dialog_suggestions.md` | Dialog suggestion style | Formal vs casual, suggestion count |\n| `query_prompt_sentiment_analysis.md` | Sentiment scoring logic | Custom sentiment categories, thresholds |\n\n**Tip:** Back up the originals before editing, so you can revert if needed.\n","# Zoom RTMS Meeting Assistant\n\nHeadless 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.\n\n## Webhook Events Handled\n\nThis skill processes two Zoom webhook events:\n\n- **`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.\n- **`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.\n\n## Webhook Dependency\n\nThis skill needs a public webhook endpoint to receive these events from Zoom.\n\n**Preferred:** Use the **ngrok-unofficial-webhook-skill** (`skills\u002Fngrok-unofficial-webhook-skill`). It auto-discovers this skill via `webhookEvents` in `skill.json`, notifies the user, and offers to route events here.\n\nOther webhook solutions (e.g. custom servers, cloud functions) will work but require additional integration to forward payloads to this service.\n\n## Prerequisites\n\n```bash\ncd skills\u002Fzoom-meeting-assistance-rtms-unofficial-community\nnpm install\n```\n\nRequires `ffmpeg` for post-meeting media conversion.\n\n## Environment Variables\n\nSet these in the skill's `.env` file:\n\n**Required:**\n- `ZOOM_SECRET_TOKEN` — Zoom webhook secret token\n- `ZOOM_CLIENT_ID` — Zoom app Client ID\n- `ZOOM_CLIENT_SECRET` — Zoom app Client Secret\n\n**Optional:**\n- `PORT` — Server port (default: `3000`)\n- `AI_PROCESSING_INTERVAL_MS` — AI analysis frequency in ms (default: `30000`)\n- `AI_FUNCTION_STAGGER_MS` — Delay between AI calls in ms (default: `5000`)\n- `AUDIO_DATA_OPT` — `1` = mixed stream, `2` = multi-stream (default: `2`)\n- `OPENCLAW_NOTIFY_CHANNEL` — Notification channel (default: `whatsapp`)\n- `OPENCLAW_NOTIFY_TARGET` — Phone number \u002F target for notifications\n\n## Starting the Service\n\n```bash\ncd skills\u002Fzoom-meeting-assistance-rtms-unofficial-community\nnode index.js\n```\n\nThis starts an Express server listening for Zoom webhook events on `PORT`.\n\n**⚠️ Important:** Before forwarding webhooks to this service, always check if it's running:\n\n```bash\n# Check if service is listening on port 3000\nlsof -i :3000\n```\n\nIf nothing is returned, start the service first before forwarding any webhook events.\n\n**Typical flow:**\n1. Start the server as a background process\n2. Zoom sends `meeting.rtms_started` webhook → service connects to RTMS WebSocket\n3. Media streams in real-time: audio, video, transcript, screenshare, chat\n4. AI processing runs periodically (dialog suggestions, sentiment, summary)\n5. `meeting.rtms_stopped` → service closes connections, generates screenshare PDF\n\n## Recorded Data\n\nAll recordings are stored organized by date:\n```\nskills\u002Fzoom-meeting-assistance-rtms-unofficial-community\u002Frecordings\u002FYYYY\u002FMM\u002FDD\u002F{streamId}\u002F\n```\n\nEach stream folder contains:\n\n| File | Content | Searchable |\n|------|---------|-----------|\n| `metadata.json` | Meeting metadata (UUID, stream ID, operator, start time) | ✅ |\n| `transcript.txt` | Plain text transcript with timestamps and speaker names | ✅ Best for searching — grep-friendly, one line per utterance |\n| `transcript.vtt` | VTT format transcript with timing cues | ✅ |\n| `transcript.srt` | SRT format transcript | ✅ |\n| `events.log` | Participant join\u002Fleave, active speaker changes (JSON lines) | ✅ |\n| `chat.txt` | Chat messages with timestamps | ✅ |\n| `ai_summary.md` | AI-generated meeting summary (markdown) | ✅ Key document — read this first for meeting overview |\n| `ai_dialog.json` | AI dialog suggestions | ✅ |\n| `ai_sentiment.json` | Sentiment analysis per participant | ✅ |\n| `mixedaudio.raw` | Mixed audio stream (raw PCM) | ❌ Binary |\n| `activespeakervideo.h264` | Active speaker video (raw H.264) | ❌ Binary |\n| `processed\u002Fscreenshare.pdf` | Deduplicated screenshare frames as PDF | ❌ Binary |\n\nAll summaries are also copied to a central folder for easy access:\n```\nskills\u002Fzoom-meeting-assistance-rtms-unofficial-community\u002Fsummaries\u002Fsummary_YYYY-MM-DDTHH-MM-SS_{streamId}.md\n```\n\n## Searching & Querying Past Meetings\n\nTo find and review past meeting data:\n\n```bash\n# List all recorded meetings by date\nls -R recordings\u002F\n\n# List meetings for a specific date\nls recordings\u002F2026\u002F01\u002F28\u002F\n\n# Search across all transcripts for a keyword\ngrep -rl \"keyword\" recordings\u002F*\u002F*\u002F*\u002F*\u002Ftranscript.txt\n\n# Search for what a specific person said\ngrep \"Chun Siong Tan\" recordings\u002F*\u002F*\u002F*\u002F*\u002Ftranscript.txt\n\n# Read a meeting summary\ncat recordings\u002FYYYY\u002FMM\u002FDD\u002F\u003CstreamId>\u002Fai_summary.md\n\n# Search summaries for a topic\ngrep -rl \"topic\" recordings\u002F*\u002F*\u002F*\u002F*\u002Fai_summary.md\n\n# Check who attended a meeting\ncat recordings\u002FYYYY\u002FMM\u002FDD\u002F\u003CstreamId>\u002Fevents.log\n\n# Get sentiment for a meeting\ncat recordings\u002FYYYY\u002FMM\u002FDD\u002F\u003CstreamId>\u002Fai_sentiment.json\n```\n\nThe `.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.\n\n## API Endpoints\n\n```bash\n# Toggle WhatsApp notifications on\u002Foff\ncurl -X POST http:\u002F\u002Flocalhost:3000\u002Fapi\u002Fnotify-toggle -H \"Content-Type: application\u002Fjson\" -d '{\"enabled\": false}'\n\n# Check notification status\ncurl http:\u002F\u002Flocalhost:3000\u002Fapi\u002Fnotify-toggle\n```\n\n## Post-Meeting Processing\n\nWhen `meeting.rtms_stopped` fires, the service automatically:\n1. Generates PDF from screenshare images\n2. Converts `mixedaudio.raw` → `mixedaudio.wav`\n3. Converts `activespeakervideo.h264` → `activespeakervideo.mp4`\n4. Muxes mixed audio + active speaker video into `final_output.mp4`\n\nManual conversion scripts are available but note that auto-conversion runs on meeting end, so manual re-runs are rarely needed.\n\n## Reading Meeting Data\n\nAfter or during a meeting, read files from `recordings\u002FYYYY\u002FMM\u002FDD\u002F{streamId}\u002F`:\n\n```bash\n# List recorded meetings by date\nls -R recordings\u002F\n\n# Read transcript\ncat recordings\u002FYYYY\u002FMM\u002FDD\u002F\u003CstreamId>\u002Ftranscript.txt\n\n# Read AI summary\ncat recordings\u002FYYYY\u002FMM\u002FDD\u002F\u003CstreamId>\u002Fai_summary.md\n\n# Read sentiment analysis\ncat recordings\u002FYYYY\u002FMM\u002FDD\u002F\u003CstreamId>\u002Fai_sentiment.json\n```\n\n## Prompt Customization\n\nWant different summary styles or analysis? Customize the AI prompts to fit your needs!\n\nEdit these files to change AI behavior:\n\n| File | Purpose | Example Customizations |\n|------|---------|----------------------|\n| `summary_prompt.md` | Meeting summary generation | Bullet points vs prose, focus areas, length |\n| `query_prompt.md` | Query response formatting | Response style, detail level |\n| `query_prompt_current_meeting.md` | Real-time meeting analysis | What to highlight during meetings |\n| `query_prompt_dialog_suggestions.md` | Dialog suggestion style | Formal vs casual, suggestion count |\n| `query_prompt_sentiment_analysis.md` | Sentiment scoring logic | Custom sentiment categories, thresholds |\n\n**Tip:** Back up the originals before editing, so you can revert if 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