据 AlphaMoat 统计,截至 2026-09,Video Messages from your openclaw 累计下载 4,479 次、获 5 个星标,在全部 5,078 个 AI 技能中排名第 2,157 位,在「设计创意」分类(512 个)中排名第 210 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 141 | 4,330 | 4 | - |
| SkillHub ↗ | 576 | 4,479 | 5 | — |
各平台的计数口径不同,统计时间不一致。
把文本或音频做成带口型同步的数字人视频,需要语音时先合成音频再交给 avatarcam 渲染成 MP4。输出可做成 Telegram 的圆形视频消息。形象与背景从 TOOLS.md 读取。无图形界面的 Linux 环境下会用虚拟显示运行,依赖 ffmpeg 与 avatarcam。安装前核对 npm 包的实际身份。生成的媒体和形象/背景素材可能通过 Telegram 发送,仅在确有分享意图时使用。应将过宽的临时文件清理改成只删除本次运行的确切输出文件。
资料发布于 2026-10-27
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 4,479 | — | 5 |
| 2026-08 | 4,213 | +4.5% | 5 |
| 2026-07 | 4,032 | +7.8% | 5 |
| 2026-06 | 3,739 | +1.1% | 4 |
| 2026-05 | 3,700 | — | 5 |
| 2026-03 | 3,396 | — | 4 |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 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 架构图一键生成
用 Nano Banana Pro 生成或编辑图片的脚本技能。
用 whisper 命令行在本地把音频转写成文字或做翻译的技能。
通过 MCP 服务生成与编辑图表的技能。
面向界面设计的思路与落地建议技能,覆盖布局、交互流程、设计令牌与前端改动。
面向多家视频模型 API 的 AI 视频生成与编排技能。
用 ffmpeg 从视频里取出画面帧,或生成便于快速查看的缩略图。
用本地 Whisper 把音频文件转成文字,模型下载完成后可完全离线运行。
数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。