algorithmic-art
Generate deterministic SVG algorithmic artwork. Invoke when the user asks for geometric,…
Generate videos using Seedance models. Invoke when user wants to create videos from text prompts, images, or reference materials.
$ npx -y skills add bytedance/agentkit-samples --skill byted-seedance-video-generate --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/byted-seedance-video-generateContext preview
The summary Claude sees to decide when to auto-load this skill.
Generate videos using Seedance models. Invoke when user wants to create videos from text prompts, images, or reference materials.
name: byted-seedance-video-generate description: Generate videos using Seedance models. Invoke when user wants to create videos from text prompts, images, or reference materials. version: 1.0.0
This skill generates videos using Doubao Seedance 1.0/1.5 models.
1. User wants to generate videos from text descriptions 2. User wants to create videos based on images (first/last frame) 3. User wants to create videos with reference materials (images, videos, audio) 4. User asks for video generation capabilities
Before using this skill, ensure the following environment variables are set:
async def video_generate(
params: list,
batch_size: int = 10,
max_wait_seconds: int = 1200,
model_name: str = None,
) -> Dict:A list of video generation requests. Each item is a dict with the following fields:
**Required per item:**
**Optional per item - Input Materials:**
**Optional per item - Video Output Parameters:**
1. **Text-to-Video**: Only provide prompt, no images/videos 2. **First Frame Guidance**: Provide first_frame for starting image 3. **First + Last Frame Guidance**: Provide both for transition video 4. **Reference Images**: Provide reference_images for style/content guidance 5. **Multimodal Reference**: Combine reference_images, reference_videos, reference_audios
The video_generate.py script will return these info:
{
"status": "success" | "partial_success" | "error",
"success_list": [{"video_name": "video_url"}],
"error_list": ["video_name"],
"error_details": [{"video_name": "...", "error": {...}}],
"pending_list": [{"video_name": "...", "task_id": "cgt-xxx", ...}]
}Based on the script return info, the final response returned to the user consists of a description of the video generation task and the video URL(s). You may download the video from the URL, but the video URL should still be provided to the user for viewing and downloading.
Note: the URL is the 'url' in the success_list of script return info. The URL must return in two ways:
1. **First, save/download the generated video to an allowed directory**:
2. **Use the `message tool` to send the video** with these parameters:
{
"action": "send",
"message": "Optional text description",
"media": "/root/.openclaw/workspace/generated-video.mp4"
}3. **Verify success**: Check that the tool returns `{"ok": true}` to confirm the video was sent successfully
4. **Normal Text** not message tool: After generation, show list of videos with Markdown format, for example:
<video src="https://example.com/video1.mp4" width="640" controls>video-1</video>
**Very important**: The video URL must be an **original online resource link** starting with `https://`, **not** a local video address you have downloaded.
You should return three types of information: 1. File format, return the video file (if you have some other methods to send the video file) and the local path of the video, for example: local_path: /root/.openclaw/workspace/skills/video-generate/xxx.mp4 2. After generation, show list of videos with Markdown format, for example:
<video src="https://example.com/video1.mp4" width="640" controls>video-1</video> <video src="https://example.com/video2.mp4" width="640" controls>video-2</video>
See [scripts/video_generate.py](scripts/video_generate.py) for the full implementation.
# Text-to-Video
python scripts/video_generate.py -p "小猫骑着滑板穿过公园" -n cat_park -r 16:9 -d 5 --resolution 720p
# First Frame Guidance
python scripts/video_generate.py -p "小猫跳起来" -n cat_jump -f "https://example.com/cat.png" -r adaptive -d 5
# First + Last Frame Guidance
python scripts/video_generate.py -p "平滑过渡动画" -n transition \
-f "https://example.com/start.png" \
-l "https://example.com/end.png" \
-d 6
# Reference Images (style/content guidance)
python scripts/video_generate.py -p "[图1]戴着眼镜的男生和[图2]柯基小狗坐在草坪上" -n styled \
--ref-images "https://example.com/boy.png" "https://example.com/dog.png" \
-r 16:9 -d欢迎来到 AgentKit 代码工坊(Samples)仓库! AgentKit 是火山引擎推出的企业级 AI Agent 开发平台,为开发者提供完整的 Agent 构建、部署和运维解决方案。平台通过标准化的开发工具链和云原生基础设施,显著降低复杂智能体应用的开发部署门槛。 本代码库包含了一系列示例和教程,帮助您理解、实现和集成 AgentKit 的各项功能到您的应用中。
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