據 AlphaMoat 統計,截至 2026-09,Video Messages from your openclaw 累計下載 4,479 次、獲 5 個星標,在全部 5,078 個 AI 技能中排名第 2,157 位,在「設計創意」分類(512 個)中排名第 210 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 141 | 4330 | 4 | - |
| SkillHub ↗ | 576 | 4479 | 5 | — |
各平台的計數口徑不同,統計時間不一致。
把文字或音訊做成帶口型同步的數位人影片,需要語音時先合成音訊再交給 avatarcam 渲染成 MP4。輸出可做成 Telegram 的圓形影片訊息。形象與背景從 TOOLS.md 讀取。無圖形介面的 Linux 環境下會用虛擬顯示執行,依賴 ffmpeg 與 avatarcam。安裝前核對 npm 套件的實際身分。產生的媒體和形象/背景素材可能透過 Telegram 傳送,僅在確有分享意圖時使用。應將範圍過大的暫存檔清理改成只刪除本次執行的確切輸出檔。
資料釋出於 2026-10-27
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 4479 | — | 5 |
| 2026-08 | 4213 | +4.5% | 5 |
| 2026-07 | 4032 | +7.8% | 5 |
| 2026-06 | 3739 | +1.1% | 4 |
| 2026-05 | 3700 | — | 5 |
| 2026-03 | 3396 | — | 4 |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 2026-10-23。
---
name: video-message
description: Generate and send video messages with a lip-syncing VRM avatar. Use when user asks for video message, avatar video, video reply, or when TTS should be delivered as video instead of audio.
metadata:
{
"openclaw":
{
"emoji": "🎥",
"requires": { "bins": ["ffmpeg", "avatarcam"] },
"install": [
{
"id": "npm",
"kind": "npm",
"package": "@thewulf7/openclaw-avatarcam",
"global": true,
"bins": ["avatarcam"],
"label": "Install avatarcam (npm)",
},
{
"id": "brew",
"kind": "brew",
"formula": "ffmpeg",
"bins": ["ffmpeg"],
"label": "Install ffmpeg (brew)",
},
{
"id": "apt",
"kind": "apt",
"packages": ["xvfb", "xauth"],
"label": "Install headless X dependencies (Linux only)",
},
],
},
}
---
# Video Message
Generate avatar video messages from text or audio. Outputs as Telegram video notes (circular format).
## Installation
```bash
npm install -g openclaw-avatarcam
```
## Configuration
Configure in `TOOLS.md`:
```markdown
### Video Message (avatarcam)
- avatar: default.vrm
- background: #00FF00
```
### Settings Reference
| Setting | Default | Description |
|---------|---------|-------------|
| `avatar` | `default.vrm` | VRM avatar file path |
| `background` | `#00FF00` | Color (hex) or image path |
## Prerequisites
### System Dependencies
| Platform | Command |
|----------|---------|
| **macOS** | `brew install ffmpeg` |
| **Linux** | `sudo apt-get install -y xvfb xauth ffmpeg` |
| **Windows** | Install ffmpeg and add to PATH |
| **Docker** | See Docker section below |
> **Note:** macOS and Windows don't need xvfb — they have native display support.
### Docker Users
Add to `OPENCLAW_DOCKER_APT_PACKAGES`:
```
build-essential procps curl file git ca-certificates xvfb xauth libgbm1 libxss1 libatk1.0-0 libatk-bridge2.0-0 libgdk-pixbuf2.0-0 libgtk-3-0 libasound2 libnss3 ffmpeg
```
## Usage
```bash
# With color background
avatarcam --audio voice.mp3 --output video.mp4 --background "#00FF00"
# With image background
avatarcam --audio voice.mp3 --output video.mp4 --background "./bg.png"
# With custom avatar
avatarcam --audio voice.mp3 --output video.mp4 --avatar "./custom.vrm"
```
## Sending as Video Note
Use OpenClaw's `message` tool with `asVideoNote`:
```
message action=send filePath=/tmp/video.mp4 asVideoNote=true
```
## Workflow
1. **Read config** from TOOLS.md (avatar, background)
2. **Generate TTS** if given text: `tts text="..."` → audio path
3. **Run avatarcam** with audio + settings → MP4 output
4. **Send as video note** via `message action=send filePath=... asVideoNote=true`
5. **Return NO_REPLY** after sending
## Example Flow
User: "Send me a video message saying hello"
```bash
# 1. TTS
tts text="Hello! How are you today?" → /tmp/voice.mp3
# 2. Generate video
avatarcam --audio /tmp/voice.mp3 --output /tmp/video.mp4 --background "#00FF00"
# 3. Send as video note
message action=send filePath=/tmp/video.mp4 asVideoNote=true
# 4. Reply
NO_REPLY
```
## Technical Details
| Setting | Value |
|---------|-------|
| Resolution | 384x384 (square) |
| Frame rate | 30fps constant |
| Max duration | 60 seconds |
| Video codec | H.264 (libx264) |
| Audio codec | AAC |
| Quality | CRF 18 (high quality) |
| Container | MP4 |
### Processing Pipeline
1. Electron renders VRM avatar with lip sync at 1280x720
2. WebM captured via `canvas.captureStream(30)`
3. FFmpeg processes: crop → fps normalize → scale → encode
4. Message tool sends via Telegram `sendVideoNote` API
## Platform Support
| Platform | Display | Notes |
|----------|---------|-------|
| macOS | Native Quartz | No extra deps |
| Linux | xvfb (headless) | `apt install xvfb` |
| Windows | Native | No extra deps |
## Headless Rendering
Avatarcam auto-detects headless environments:
- Uses `xvfb-run` when `$DISPLAY` is not set (Linux only)
- macOS/Windows use native display
- GPU stall warnings are safe to ignore
- Generation time: ~1.5x realtime (20s audio ≈ 30s processing)
## Notes
- Config is read from TOOLS.md
- Clean up temp files after sending: `rm /tmp/video*.mp4`
- For regular video (not circular), omit `asVideoNote=true`
ContextWeave 架構圖一鍵生成
用 Nano Banana Pro 生成或編輯圖片的指令碼技能。
用 whisper 命令列工具在本機將音訊轉成文字或進行翻譯。
透過 MCP 服務生成與編輯圖表的技能。
面向介面設計的思路與落地建議技能,覆蓋佈局、互動流程、設計令牌與前端改動。
面向多家影片模型 API 的 AI 影片生成與編排技能。
用 ffmpeg 從影片裡取出畫面幀,或生成便於快速檢視的縮圖。
用本地 Whisper 把音訊檔案轉成文字,模型下載完成後可完全離線執行。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。