📸 Vision Helper — Image Analysis#

Analyze images using vision models via Ollama, with extended timeout support for cloud-based models.

Why Not Use the Built-in image Tool?#

The built-in image tool has limited timeout settings that cause failures with cloud vision models (which often need 40–120 seconds). This skill calls the Ollama API directly with a 180-second timeout, supporting both local and cloud models reliably.

It also bypasses the built-in tool's file path restrictions, allowing analysis of images from any readable directory.

Usage#

Basic#

bash

# Analyze an image (default: English description)
python3 <skill-dir>/scripts/analyze_image.py <image_path>

# With a custom prompt
python3 <skill-dir>/scripts/analyze_image.py <image_path> "Is this a chess game? Describe the board state"

# With a specific model
python3 <skill-dir>/scripts/analyze_image.py <image_path> "Describe content" kimi-k2.5:cloud

<skill-dir> resolves to your OpenClaw skill installation directory, typically ~/.openclaw/workspace/skills/vision-helper/.

In Conversation#

When you need to analyze an image, use the exec tool:

text

exec: python3 <skill-dir>/scripts/analyze_image.py /path/to/image.png "What do you see?"

Important: Set exec timeout to 120–180 seconds, as cloud vision models are slow.

Screenshot + Analysis Workflow#

Option A: Browser screenshot → analyze

text

1. browser(action="screenshot") → get screenshot path (MEDIA: xxx)
2. exec("<skill-dir>/scripts/analyze_image.py <screenshot_path> 'Describe this UI'")
3. Act on the analysis result

Option B: Desktop screenshot → analyze

macOS:

text

1. exec("screencapture -x /tmp/screen.png")
2. exec("<skill-dir>/scripts/analyze_image.py /tmp/screen.png 'Describe the desktop'")

Linux:

text

1. exec("gnome-screenshot -f /tmp/screen.png")
   — or —
   exec("import /tmp/screen.png")  # ImageMagick
   — or —
   exec("scrot /tmp/screen.png")
2. exec("<skill-dir>/scripts/analyze_image.py /tmp/screen.png 'Describe the desktop'")

Option C: Game/App UI → analyze → act

text

1. Screenshot the current screen
2. Use vision-helper to identify UI elements, buttons, text
3. Execute clicks/input based on the analysis

Environment Variables#

VariableDefaultDescriptionVISION_MODEL``gemma4:31bDefault vision modelVISION_TIMEOUT``180Request timeout in secondsOLLAMA_API_URL``http://localhost:11434/api/chatOllama API endpoint

Supported Models#

ModelVisionSpeedRecommendationgemma4:31b✅Local, fast⭐ Primary (privacy, no API needed)kimi-k2.6:cloud✅40–120s🔬 Advanced (high quality, cloud)kimi-k2.5:cloud✅40–90sAlternative cloud optionqwen3.5:cloud✅30–60sFast cloud recognitionqwen3.5:397b-cloud✅40–90sHigh quality cloudgemma4:31b✅Local, fastPrivacy-first (runs offline)

Note: Cloud models require the model to be available in your Ollama instance. Use VISION_MODEL env var to switch.

FAQ#

Q: Can I use the built-in image tool instead?#

A: It works for local models but will time out on cloud vision models. Always prefer this skill's script for reliable results.

Q: What image formats are supported?#

A: PNG, JPG, JPEG, GIF, WebP, BMP, TIFF, SVG. Maximum file size: 20 MB.

Q: Where should I save screenshots?#

A: Any readable directory works — /tmp/, your workspace, etc. This script has no path restrictions.

Q: How do I use a Chinese prompt?#

A: Pass it as the second argument: python3 <skill-dir>/scripts/analyze_image.py /tmp/img.png "请描述这张图片的内容"

Automation Ideas#

• Game automation: Screenshot → analyze game state → decide next action
• Browser verification: Screenshot → verify page loaded correctly
• Desktop monitoring: Periodic screenshots → detect changes
• UI testing: Screenshot → verify rendered output
• OCR: Extract text content from images