qwen-vision
GitHub弥补Claude原生视频理解缺失，通过调用Qwen Omni模型实现视频与图像分析。支持动作识别、内容描述及多轮对话，需配置API密钥并安装依赖。
Trigger Scenarios
Install
npx skills add davepoon/buildwithclaude --skill qwen-vision -g -y
SKILL.md
Frontmatter
{
"name": "qwen-vision",
"version": "0.1.0",
"description": "Use when the user asks to \"analyze video\", \"watch this video\", \"what happens in this video\", \"describe this clip\", \"review this footage\", \"classify these videos\", \"compare videos\", \"analyze this image\", \"what's in this screenshot\", or when the user provides a video\/image file path and expects visual understanding. Also trigger on: \"qwen\", \"video bridge\", \"multimodal analysis\", \"motion analysis\", \"video reference\", \"video breakdown\", \"batch classify\", or any task requiring understanding of video content that Claude cannot do natively.\n"
}
Qwen Vision Bridge
Claude cannot natively understand video. This skill bridges that gap by calling Qwen Omni — a natively multimodal model that processes video with temporal attention (it sees motion, not just individual frames).
The bridge also handles images, useful when you want Qwen's analysis on screenshots, diagrams, or photos.
How it works
A Python script at ${CLAUDE_PLUGIN_ROOT}/skills/qwen-vision/scripts/qwen_bridge.py sends media files to the Qwen API and returns the analysis as text. Call it via Bash.
Prerequisites
The user must have:
DASHSCOPE_API_KEYenvironment variable set (get one at https://dashscope.console.aliyun.com/ or https://modelstudio.console.alibabacloud.com/)- Python 3.9+ with
dashscopepackage installed
If the user hasn't set up yet, suggest running /qwen-setup first.
Basic usage
python3 "${CLAUDE_PLUGIN_ROOT}/skills/qwen-vision/scripts/qwen_bridge.py" "/path/to/video.mp4" "Describe what happens in this video"
Parameters
| Flag | Default | Description |
|---|---|---|
| (positional 1) | required | Path to video or image file |
| (positional 2) | generic prompt | Analysis prompt |
--fps |
2.0 | Frames per second to sample from video. Lower = cheaper, higher = more detail |
--model |
qwen-omni-plus-latest | Qwen model to use |
--json |
off | Output as JSON (for parsing) |
--context |
none | Path to JSON file with previous conversation (multi-turn) |
--save-context |
none | Save conversation context for follow-up questions |
--system-prompt |
none | Custom system prompt for Qwen |
--prompt-file |
none | Read prompt from a file instead of argument |
Supported formats
Video: .mp4, .mov, .avi, .mkv, .webm, .flv, .wmv Image: .png, .jpg, .jpeg, .gif, .webp, .bmp, .tiff
Patterns
Single video analysis
python3 "${CLAUDE_PLUGIN_ROOT}/skills/qwen-vision/scripts/qwen_bridge.py" "/path/to/video.mp4" "Describe the character's body movement, poses, and transitions" --fps 2
Parse the text response and use it in your answer to the user.
Batch analysis
When the user has multiple videos to analyze, write a Python script that loops through files and calls the bridge for each one. Use --json flag for machine-readable output. See references/batch-pattern.md for a template.
Multi-turn (follow-up questions)
# First question
python3 "${CLAUDE_PLUGIN_ROOT}/skills/qwen-vision/scripts/qwen_bridge.py" video.mp4 "General analysis" --save-context /tmp/ctx.json
# Follow-up
python3 "${CLAUDE_PLUGIN_ROOT}/skills/qwen-vision/scripts/qwen_bridge.py" video.mp4 "Tell me more about the lighting" --context /tmp/ctx.json
Image analysis
Same script, just pass an image path instead of video:
python3 "${CLAUDE_PLUGIN_ROOT}/skills/qwen-vision/scripts/qwen_bridge.py" "/path/to/screenshot.png" "What UI elements are visible in this screenshot?"
Cost-saving tips
- Use
--fps 1for long videos or when fine detail isn't needed - Use
--fps 0.5for very long videos (minutes+) - For batch jobs, start with
--fps 1and increase only if results are too vague
Error handling
- If
DASHSCOPE_API_KEYis not set, the script exits with a clear error message. Guide the user to set it up. - If
dashscopeis not installed, suggestpip install dashscope. - If the API returns an error, the script prints the error code and message. Common issues: invalid key, quota exceeded, unsupported file format.
- If a video file is too large for the API, suggest lowering
--fpsor trimming the video first.
What Qwen sees vs what Claude sees
This is important context for the user: Qwen processes video frames with temporal attention — it understands motion, direction, rhythm, and transitions between frames. Claude analyzing individual screenshots cannot do this. When the user needs to understand what happens in a video (not just what a single frame looks like), this bridge is the right tool.
Additional resources
references/batch-pattern.md— template for batch video classificationreferences/prompt-tips.md— effective prompts for different analysis types
Version History
- 502fc01 Current 2026-07-05 15:11
