qwen-vision

Documents

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.

QUICK START

How to use this skill

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Prompt to paste

I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/davepoon/buildwithclaude/blob/HEAD/plugins/give-claude-eyes/skills/qwen-vision/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/qwen-vision/. Do not write files or run scripts until I approve.

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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_KEY environment variable set (get one at https://dashscope.console.aliyun.com/ or https://modelstudio.console.alibabacloud.com/)
Python 3.9+ with dashscope package 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 1 for long videos or when fine detail isn't needed
Use --fps 0.5 for very long videos (minutes+)
For batch jobs, start with --fps 1 and increase only if results are too vague

Error handling

If DASHSCOPE_API_KEY is not set, the script exits with a clear error message. Guide the user to set it up.
If dashscope is not installed, suggest pip 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 --fps or 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 classification
references/prompt-tips.md — effective prompts for different analysis types