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# OpenAI Image Vision - Usage Examples
## Setup
Set up your API credentials using the agent's env_config tool:
```bash
# Set your OpenAI API key
env_config(action="set", key="OPENAI_API_KEY", value="sk-your-api-key-here")
# Optional: Set custom API base URL (for proxy or compatible services)
env_config(action="set", key="OPENAI_API_BASE", value="https://api.openai.com/v1")
```
## Example 1: Analyze a Local Image
```bash
bash scripts/vision.sh "/path/to/photo.jpg" "What's in this image?"
```
**Expected Output:**
```json
{
"model": "gpt-4.1-mini",
"content": "The image shows a beautiful landscape with mountains in the background and a lake in the foreground. The sky is clear with some clouds, and there are trees along the shoreline.",
"usage": {
"prompt_tokens": 1234,
"completion_tokens": 45,
"total_tokens": 1279
}
}
```
## Example 2: Analyze an Image from URL
```bash
bash scripts/vision.sh "https://example.com/image.jpg" "Describe this image in detail"
```
## Example 3: Extract Text (OCR)
```bash
bash scripts/vision.sh "document.png" "Extract all text from this image"
```
**Use Case:** Extract text from screenshots, scanned documents, or photos of text.
## Example 4: Identify Objects
```bash
bash scripts/vision.sh "scene.jpg" "List all objects you can identify in this image"
```
## Example 5: Analyze Colors and Composition
```bash
bash scripts/vision.sh "artwork.jpg" "Describe the color palette and composition of this image"
```
## Example 6: Count Items
```bash
bash scripts/vision.sh "crowd.jpg" "How many people are in this image?"
```
## Example 7: Use Different Models
```bash
# Use gpt-4.1-mini (default, latest mini model)
bash scripts/vision.sh "image.jpg" "Analyze this" "gpt-4.1-mini"
# Use gpt-4.1 (most capable, best for complex analysis)
bash scripts/vision.sh "image.jpg" "Analyze this" "gpt-4.1"
# Use gpt-4o-mini (previous mini model)
bash scripts/vision.sh "image.jpg" "Analyze this" "gpt-4o-mini"
```
## Example 8: Complex Analysis
```bash
bash scripts/vision.sh "product.jpg" "Analyze this product image. Describe the product, its features, colors, and suggest what kind of marketing copy would work well for it."
```
## Example 9: Safety and Content Moderation
```bash
bash scripts/vision.sh "content.jpg" "Is there any inappropriate or unsafe content in this image?"
```
## Example 10: Technical Analysis
```bash
bash scripts/vision.sh "diagram.png" "Explain what this technical diagram represents and how it works"
```
## Integration with Agent
When the agent loads this skill, it will be available in the `<available_skills>` section. The agent can use it like:
```bash
bash "<base_dir>/scripts/vision.sh" "user_uploaded_image.jpg" "What's in this image?"
```
The `<base_dir>` will be automatically provided by the skill system.
## Error Handling Examples
### Missing API Key
```bash
$ bash scripts/vision.sh "image.jpg" "What is this?"
{"error": "OPENAI_API_KEY environment variable is not set", "help": "Visit https://platform.openai.com/api-keys to get an API key"}
```
### File Not Found
```bash
$ bash scripts/vision.sh "nonexistent.jpg" "What is this?"
{"error": "Image file not found", "path": "nonexistent.jpg"}
```
### Unsupported Format
```bash
$ bash scripts/vision.sh "file.bmp" "What is this?"
{"error": "Unsupported image format", "extension": "bmp", "supported": ["jpg", "jpeg", "png", "gif", "webp"]}
```
### Missing Parameters
```bash
$ bash scripts/vision.sh
{"error": "Image path or URL is required", "usage": "bash vision.sh <image_path_or_url> <question> [model]"}
```
## Tips for Best Results
1. **Be Specific**: Ask clear, specific questions about what you want to know
2. **Image Quality**: Higher quality images generally produce better results
3. **Model Selection**:
- Use `gpt-4.1` for complex analysis requiring highest accuracy
- Use `gpt-4.1-mini` (default) for most tasks - latest mini model with good balance
4. **Text Extraction**: For OCR tasks, ensure text is clearly visible and not too small
5. **Multiple Aspects**: You can ask about multiple things in one question
6. **Context**: Provide context in your question if needed (e.g., "This is a medical scan, what do you see?")
## Performance Notes
- **Local Files**: Automatically base64-encoded, adds ~33% size overhead
- **URLs**: Passed directly to API, no encoding overhead
- **Timeout**: 60 seconds for API calls
- **Max Tokens**: 1000 tokens for responses (configurable in script)
- **Rate Limits**: Subject to your OpenAI API plan
## Supported Image Formats
✅ JPEG (`.jpg`, `.jpeg`)
✅ PNG (`.png`)
✅ GIF (`.gif`)
✅ WebP (`.webp`)
❌ BMP, TIFF, SVG, and other formats are not supported
## Cost Considerations
Vision API calls cost more than text-only calls because they include image tokens. Costs vary by:
- Model used (gpt-4.1 vs gpt-4.1-mini)
- Image size and resolution
- Length of response
Check OpenAI's pricing page for current rates: https://openai.com/pricing

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# OpenAI Image Vision Skill
This skill enables image analysis using OpenAI's Vision API (GPT-4 Vision models).
## Features
- ✅ Analyze images from local files or URLs
- ✅ Support for multiple image formats (JPEG, PNG, GIF, WebP)
- ✅ Automatic base64 encoding for local files
- ✅ Direct URL passing for remote images
- ✅ Configurable model selection
- ✅ Custom API base URL support
- ✅ Pure bash/curl implementation (no Python dependencies)
## Quick Start
1. **Set up API credentials using env_config:**
```bash
env_config(action="set", key="OPENAI_API_KEY", value="sk-your-api-key-here")
# Optional: custom API base
env_config(action="set", key="OPENAI_API_BASE", value="https://api.openai.com/v1")
```
2. **Analyze an image:**
```bash
bash scripts/vision.sh "/path/to/photo.jpg" "What's in this image?"
```
3. **Analyze from URL:**
```bash
bash scripts/vision.sh "https://example.com/image.jpg" "Describe this image"
```
```bash
bash scripts/vision.sh "/path/to/image.jpg" "What's in this image?"
```
3. **Analyze from URL:**
```bash
bash scripts/vision.sh "https://example.com/image.jpg" "Describe this image"
```
## Usage Examples
### Basic image analysis
```bash
bash scripts/vision.sh "photo.jpg" "What objects can you see?"
```
### Text extraction (OCR)
```bash
bash scripts/vision.sh "document.png" "Extract all text from this image"
```
### Detailed description
```bash
bash scripts/vision.sh "scene.jpg" "Describe this scene in detail, including colors, mood, and composition"
```
### Using different models
```bash
# Use gpt-4.1-mini (default, latest mini model)
bash scripts/vision.sh "image.jpg" "Analyze this" "gpt-4.1-mini"
# Use gpt-4.1 (most capable, latest model)
bash scripts/vision.sh "image.jpg" "Analyze this" "gpt-4.1"
# Use gpt-4o-mini (previous mini model)
bash scripts/vision.sh "image.jpg" "Analyze this" "gpt-4o-mini"
```
## Environment Variables
| Variable | Required | Default | Description |
|----------|----------|---------|-------------|
| `OPENAI_API_KEY` | No* | - | OpenAI API key (preferred) |
| `OPENAI_API_BASE` | No | `https://api.openai.com/v1` | Custom OpenAI API base URL |
| `LINKAI_API_KEY` | No* | - | LinkAI API key (fallback when OPENAI_API_KEY is not set) |
| `LINKAI_API_BASE` | No | `https://api.link-ai.tech` | LinkAI API base URL |
\* At least one of `OPENAI_API_KEY` or `LINKAI_API_KEY` must be set. OpenAI takes priority when both are configured.
## Response Format
Success response:
```json
{
"model": "gpt-4.1-mini",
"content": "The image shows a beautiful sunset over mountains...",
"usage": {
"prompt_tokens": 1234,
"completion_tokens": 567,
"total_tokens": 1801
}
}
```
Error response:
```json
{
"error": "Error description",
"details": "Additional information"
}
```
## Supported Models
- `gpt-4.1-mini` (default) - Latest mini model, fast and cost-effective
- `gpt-4.1` - Latest GPT-4 variant, most capable
- `gpt-4o-mini` - Previous generation mini model
- `gpt-4-turbo` - Previous generation turbo model
## Supported Image Formats
- JPEG (`.jpg`, `.jpeg`)
- PNG (`.png`)
- GIF (`.gif`)
- WebP (`.webp`)
## Technical Details
- **Implementation**: Pure bash script using curl and base64
- **Timeout**: 60 seconds for API calls
- **Max tokens**: 1000 tokens for responses
- **Image handling**:
- Local files are automatically base64-encoded
- URLs are passed directly to the API
- MIME types are auto-detected from file extensions
## Error Handling
The script handles various error cases:
- Missing required parameters
- Missing API key
- File not found
- Unsupported image formats
- API errors
- Network timeouts
- Invalid JSON responses
## Integration with Agent System
When loaded by the agent system, this skill will appear in `<available_skills>` with a `<base_dir>` path. Use it like:
```bash
bash "<base_dir>/scripts/vision.sh" "image.jpg" "What's in this image?"
```
The agent will automatically:
- Load environment variables from `~/.cow/.env`
- Provide the correct `<base_dir>` path
- Handle skill discovery and registration
## Notes
- Images are sent to OpenAI's servers for processing
- Large images may be automatically resized by the API
- Rate limits depend on your OpenAI API plan
- Token usage includes both the image and text in the prompt
- Base64 encoding increases the size of local images by ~33%
## Troubleshooting
**"OPENAI_API_KEY environment variable is not set"**
- Set the environment variable using env_config tool
- Or use the agent's env_config tool
**"Image file not found"**
- Check the file path is correct
- Use absolute paths or paths relative to current directory
**"Unsupported image format"**
- Only JPEG, PNG, GIF, and WebP are supported
- Check the file extension matches the actual format
**"Failed to call OpenAI API"**
- Check your internet connection
- Verify the API key is valid
- Check if custom API base URL is correct
## License
Part of the chatgpt-on-wechat project.

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When editing the (newly-generated or existing) skill, remember that the skill is being created for another instance of the agent to use. Include information that would be beneficial and non-obvious to the agent. Consider what procedural knowledge, domain-specific details, or reusable assets would help another agent instance execute these tasks more effectively. When editing the (newly-generated or existing) skill, remember that the skill is being created for another instance of the agent to use. Include information that would be beneficial and non-obvious to the agent. Consider what procedural knowledge, domain-specific details, or reusable assets would help another agent instance execute these tasks more effectively.
#### Learn Proven Design Patterns #### Design Patterns
Consult these helpful guides based on your skill's needs: **Workflow patterns** — For complex tasks, break operations into sequential steps or conditional branches:
- **Multi-step processes**: See references/workflows.md for sequential workflows and conditional logic ```markdown
- **Specific output formats or quality standards**: See references/output-patterns.md for template and example patterns # Sequential: list numbered steps with scripts
1. Analyze the form (run analyze_form.py)
2. Create field mapping (edit fields.json)
3. Fill the form (run fill_form.py)
These files contain established best practices for effective skill design. # Conditional: guide through decision points
1. Determine the modification type:
**Creating new content?** → Follow "Creation workflow"
**Editing existing content?** → Follow "Editing workflow"
```
**Output patterns** — When consistent output format matters, provide a template or input/output examples in SKILL.md so the agent can follow the desired style.
#### Start with Reusable Skill Contents #### Start with Reusable Skill Contents

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# Output Patterns
Use these patterns when skills need to produce consistent, high-quality output.
## Template Pattern
Provide templates for output format. Match the level of strictness to your needs.
**For strict requirements (like API responses or data formats):**
```markdown
## Report structure
ALWAYS use this exact template structure:
# [Analysis Title]
## Executive summary
[One-paragraph overview of key findings]
## Key findings
- Finding 1 with supporting data
- Finding 2 with supporting data
- Finding 3 with supporting data
## Recommendations
1. Specific actionable recommendation
2. Specific actionable recommendation
```
**For flexible guidance (when adaptation is useful):**
```markdown
## Report structure
Here is a sensible default format, but use your best judgment:
# [Analysis Title]
## Executive summary
[Overview]
## Key findings
[Adapt sections based on what you discover]
## Recommendations
[Tailor to the specific context]
Adjust sections as needed for the specific analysis type.
```
## Examples Pattern
For skills where output quality depends on seeing examples, provide input/output pairs:
```markdown
## Commit message format
Generate commit messages following these examples:
**Example 1:**
Input: Added user authentication with JWT tokens
Output:
```
feat(auth): implement JWT-based authentication
Add login endpoint and token validation middleware
```
**Example 2:**
Input: Fixed bug where dates displayed incorrectly in reports
Output:
```
fix(reports): correct date formatting in timezone conversion
Use UTC timestamps consistently across report generation
```
Follow this style: type(scope): brief description, then detailed explanation.
```
Examples help Claude understand the desired style and level of detail more clearly than descriptions alone.

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# Workflow Patterns
## Sequential Workflows
For complex tasks, break operations into clear, sequential steps. It is often helpful to give Claude an overview of the process towards the beginning of SKILL.md:
```markdown
Filling a PDF form involves these steps:
1. Analyze the form (run analyze_form.py)
2. Create field mapping (edit fields.json)
3. Validate mapping (run validate_fields.py)
4. Fill the form (run fill_form.py)
5. Verify output (run verify_output.py)
```
## Conditional Workflows
For tasks with branching logic, guide Claude through decision points:
```markdown
1. Determine the modification type:
**Creating new content?** → Follow "Creation workflow" below
**Editing existing content?** → Follow "Editing workflow" below
2. Creation workflow: [steps]
3. Editing workflow: [steps]
```