| name | image-analysis |
| description | Analyze, describe, or answer questions about image content using vision AI. Use this skill when the user asks anything about an image — what it shows, what's in it, reading text/diagrams, identifying objects, or any question referencing a previous image (e.g. 'what is in this image', 'read the text in this screenshot', 'explain this diagram', 'tell me about the image above', '识别图片', '分析图片内容'). |
Image Analysis Skill
Analyze images via LLM provider's chat completions API with vision.
When to Use
Trigger this skill when the user asks to:
- Describe or analyze an image ("describe this image", "what's in this picture")
- Recognize content in an image ("识别图片", "分析图片内容")
- Extract text from an image (OCR) ("read the text in this screenshot")
- Answer questions about an image ("how many people are in this photo", "what color is the car")
- Reference a previous image ("tell me about the image above", "what's in the previous picture")
- Explain visual content ("explain this diagram", "what does this chart show")
- Identify objects ("what kind of plant is this", "identify this")
Steps
Step 1: Identify the image
Get the image from the user's message or workspace context.
- If the user provides a local file path, use it directly.
- If the user provides an image URL (http/https), ask the user whether they'd like to download the image to a local file first, then download it (e.g. with
curl -sL "<url>" -o /tmp/image_to_analyze.<ext>) before proceeding.
- If the user references a previous image without a path (e.g. "the above image", "the image I just sent"), search the workspace directory for recent image files:
- List files in the workspace matching common image extensions (
*.png, *.jpg, *.jpeg, *.gif, *.webp, *.bmp)
- Sort by modification time (most recent first)
- Use the most recent image. If there are multiple candidates and it's ambiguous, ask the user which image they mean.
Step 2: Run the image analysis script
bash scripts/analyze_image.sh "<image_path>" "<question or prompt>"
The script reads API credentials and model from sudoclaw.json via the SUDOCLAW_CONFIG_PATH env var (auto-set at gateway startup). No manual env var configuration is needed.
The script prints the analysis result (LLM response) on success, or an error message on failure.
Step 3: Show the result
Display the analysis result to the user.
