/video-understand
Implement specialized video understanding capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to analyze video content, understand motion and temporal sequences, extract information from video frames, describe video scenes, or perform video-based AI
$ npx -y skills add jjyaoao/helloagents --skill video-understand --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/video-understand
Context preview
The summary Claude sees to decide when to auto-load this skill.
Implement specialized video understanding capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to analyze video content, understand motion and temporal sequences, extract information from video frames, describe video scenes, or perform video-based AI
SKILL.md
video-understand.SKILL.mdname: video-understand
description: Implement specialized video understanding capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to analyze video content, understand motion and temporal sequences, extract information from video frames, describe video scenes, or perform video-based AI analysis. Optimized for MP4, AVI, MOV, and other common video formats.
license: MIT
Video Understanding Skill
This skill provides specialized video understanding functionality using the z-ai-web-dev-sdk package, enabling AI models to analyze, describe, and extract information from video content including motion, temporal sequences, and scene changes.
Skills Path
**Skill Location**: `{project_path}/skills/video-understand`
this skill is located at above path in your project.
**Reference Scripts**: Example test scripts are available in the `{Skill Location}/scripts/` directory for quick testing and reference. See `{Skill Location}/scripts/video-understand.ts` for a working example.
Overview
Video Understanding focuses specifically on video content analysis, providing capabilities for:
- Video scene understanding and description
- Action and motion detection
- Temporal sequence analysis
- Event detection in videos
- Video content summarization
- Scene change detection
- People and object tracking across frames
- Audio-visual content analysis (when applicable)
**IMPORTANT**: z-ai-web-dev-sdk MUST be used in backend code only. Never use it in client-side code.
Prerequisites
The z-ai-web-dev-sdk package is already installed. Import it as shown in the examples below.
CLI Usage (For Simple Tasks)
For quick video analysis tasks, you can use the z-ai CLI instead of writing code. This is ideal for simple video descriptions, testing, or automation.
Basic Video Analysis
# Analyze a video from URL
z-ai vision --prompt "Summarize what happens in this video" --image "https://example.com/video.mp4"
# Note: Use --image flag for video URLs as well
z-ai vision -p "Describe the key events" -i "https://example.com/presentation.mp4"
Analyze Local Videos
# Analyze a local video file
z-ai vision -p "What activities are shown in this video?" -i "./recording.mp4"
# Save response to file
z-ai vision -p "Provide a detailed summary" -i "./meeting.mp4" -o summary.json
Advanced Video Analysis
# Complex scene understanding with thinking
z-ai vision \
-p "Analyze this video and identify: 1) Main events, 2) People and their actions, 3) Timeline of key moments" \
-i "./event.mp4" \
--thinking \
-o analysis.json
# Action detection
z-ai vision \
-p "Identify all actions performed by people in this video" \
-i "./sports.mp4" \
--thinking
Streaming Output
# Stream the video analysis
z-ai vision -p "Describe this video content" -i "./video.mp4" --stream
CLI Parameters
- `--prompt, -p <text>`: **Required** - Question or instruction about the video
- `--image, -i <URL or path>`: Optional - Video URL or local file path (despite the name, it works for videos too)
- `--thinking, -t`: Optional - Enable chain-of-thought reasoning for complex analysis (default: disabled)
- `--output, -o <path>`: Optional - Output file path (JSON format)
- `--stream`: Optional - Stream the response in real-time
Supported Video Formats
- MP4 (.mp4) - Most widely supported format
- AVI (.avi) - Audio Video Interleave
- MOV (.mov) - QuickTime format
- WebM (.webm) - Web-optimized format
- MKV (.mkv) - Matroska format
- FLV (.flv) - Flash Video format
When to Use CLI vs SDK
**Use CLI for:**
- Quick video summaries
- One-off video analysis
- Testing video understanding capabilities
- Simple automation scripts
- Generating video descriptions
**Use SDK for:**
- Multi-turn conversations about videos
- Complex video processing pipelines
- Production applications with error handling
- Custom integration with video processing logic
- Batch video processing with custom workflows
Recommended Approach
For better performance and reliability with local videos, consider: 1. Uploading videos to a CDN and using URLs 2. For shorter videos, convert key frames to images for faster analysis 3. For long videos, consider chunking or sampling at intervals
Basic Video Understanding Implementation
Single Video Analysis
import ZAI from 'z-ai-web-dev-sdk';
async function analyzeVideo(videoUrl, prompt) {
const zai = await ZAI.create();
const response = await zai.chat.completions.createVision({
messages: [
{
role: 'user',
content: [
{
type: 'text',
text: prompt
},
{
type: 'video_url',
video_url: {
url: videoUrl
}
}
]
}
],
thinking: { type: 'disabled' }
});
return response.choices[0]?.message?.content;
}
// Usage examples
const summary = await analyzeVideo(
'https://example.com/presentation.mp4',
'Summarize the key points presented in this video'
);
const actionDetection = await analyzeVideo(
'https://example.com/sports.mp4',
'Identify and describe all athletic actions performed in this video'
);Video Scene Understanding
import ZAI from 'z-ai-web-dev-sdk';
async function understandVideoScenes(videoUrl) {
const zai = await ZAI.create();
const prompt = `Analyze this video and provide:
1. Overall summary of the video content
2. Main scenes or segments (with approximate timestamps if possible)
3. Key people or characters and their roles
4. Important actions or events in chronological order
5. Setting and environment description
6. Overall mood or tone`;
const response = await zai.chat.completions.createVision({
messages: [
{
role: 'user',
content: [
{ type: 'text', text: prompt },
{ type: 'video_url', video_url: { url: videoUrl } }
]
}
],Read more
name: video-understand description: Implement specialized video understanding capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to analyze video content, understand motion and temporal sequences, extract information from video frames, describe video scenes, or perform video-based AI analysis. Optimized for MP4, AVI, MOV, and other common video formats. license: MIT
Video Understanding Skill
This skill provides specialized video understanding functionality using the z-ai-web-dev-sdk package, enabling AI models to analyze, describe, and extract information from video content including motion, temporal sequences, and scene changes.
Skills Path
**Skill Location**: `{project_path}/skills/video-understand`
this skill is located at above path in your project.
**Reference Scripts**: Example test scripts are available in the `{Skill Location}/scripts/` directory for quick testing and reference. See `{Skill Location}/scripts/video-understand.ts` for a working example.
Overview
Video Understanding focuses specifically on video content analysis, providing capabilities for:
- Video scene understanding and description
- Action and motion detection
- Temporal sequence analysis
- Event detection in videos
- Video content summarization
- Scene change detection
- People and object tracking across frames
- Audio-visual content analysis (when applicable)
**IMPORTANT**: z-ai-web-dev-sdk MUST be used in backend code only. Never use it in client-side code.
Prerequisites
The z-ai-web-dev-sdk package is already installed. Import it as shown in the examples below.
CLI Usage (For Simple Tasks)
For quick video analysis tasks, you can use the z-ai CLI instead of writing code. This is ideal for simple video descriptions, testing, or automation.
Basic Video Analysis
# Analyze a video from URL z-ai vision --prompt "Summarize what happens in this video" --image "https://example.com/video.mp4" # Note: Use --image flag for video URLs as well z-ai vision -p "Describe the key events" -i "https://example.com/presentation.mp4"
Analyze Local Videos
# Analyze a local video file z-ai vision -p "What activities are shown in this video?" -i "./recording.mp4" # Save response to file z-ai vision -p "Provide a detailed summary" -i "./meeting.mp4" -o summary.json
Advanced Video Analysis
# Complex scene understanding with thinking z-ai vision \ -p "Analyze this video and identify: 1) Main events, 2) People and their actions, 3) Timeline of key moments" \ -i "./event.mp4" \ --thinking \ -o analysis.json # Action detection z-ai vision \ -p "Identify all actions performed by people in this video" \ -i "./sports.mp4" \ --thinking
Streaming Output
# Stream the video analysis z-ai vision -p "Describe this video content" -i "./video.mp4" --stream
CLI Parameters
- `--prompt, -p <text>`: **Required** - Question or instruction about the video
- `--image, -i <URL or path>`: Optional - Video URL or local file path (despite the name, it works for videos too)
- `--thinking, -t`: Optional - Enable chain-of-thought reasoning for complex analysis (default: disabled)
- `--output, -o <path>`: Optional - Output file path (JSON format)
- `--stream`: Optional - Stream the response in real-time
Supported Video Formats
- MP4 (.mp4) - Most widely supported format
- AVI (.avi) - Audio Video Interleave
- MOV (.mov) - QuickTime format
- WebM (.webm) - Web-optimized format
- MKV (.mkv) - Matroska format
- FLV (.flv) - Flash Video format
When to Use CLI vs SDK
**Use CLI for:**
- Quick video summaries
- One-off video analysis
- Testing video understanding capabilities
- Simple automation scripts
- Generating video descriptions
**Use SDK for:**
- Multi-turn conversations about videos
- Complex video processing pipelines
- Production applications with error handling
- Custom integration with video processing logic
- Batch video processing with custom workflows
Recommended Approach
For better performance and reliability with local videos, consider: 1. Uploading videos to a CDN and using URLs 2. For shorter videos, convert key frames to images for faster analysis 3. For long videos, consider chunking or sampling at intervals
Basic Video Understanding Implementation
Single Video Analysis
import ZAI from 'z-ai-web-dev-sdk';
async function analyzeVideo(videoUrl, prompt) {
const zai = await ZAI.create();
const response = await zai.chat.completions.createVision({
messages: [
{
role: 'user',
content: [
{
type: 'text',
text: prompt
},
{
type: 'video_url',
video_url: {
url: videoUrl
}
}
]
}
],
thinking: { type: 'disabled' }
});
return response.choices[0]?.message?.content;
}
// Usage examples
const summary = await analyzeVideo(
'https://example.com/presentation.mp4',
'Summarize the key points presented in this video'
);
const actionDetection = await analyzeVideo(
'https://example.com/sports.mp4',
'Identify and describe all athletic actions performed in this video'
);Video Scene Understanding
import ZAI from 'z-ai-web-dev-sdk';
async function understandVideoScenes(videoUrl) {
const zai = await ZAI.create();
const prompt = `Analyze this video and provide:
1. Overall summary of the video content
2. Main scenes or segments (with approximate timestamps if possible)
3. Key people or characters and their roles
4. Important actions or events in chronological order
5. Setting and environment description
6. Overall mood or tone`;
const response = await zai.chat.completions.createVision({
messages: [
{
role: 'user',
content: [
{ type: 'text', text: prompt },
{ type: 'video_url', video_url: { url: videoUrl } }
]
}
],🤖 生产级多智能体框架 - 工具响应协议、上下文工程、会话持久化、子代理机制等16项核心能力 HelloAgents 是一个基于 OpenAI 原生 API 构建的生产级多智能体框架,集成了工具响应协议(ToolResponse)、上下文工程(HistoryManager/TokenCounter)、会话持久化(SessionStore)、子代理机制(TaskTool)、乐观锁(文件编辑)、熔断器(CircuitBreaker)、Skills 知识外化、TodoWrite 进度管理、DevLog
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