Smart Drawing Image Understanding
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Please follow https://skillhub.cn/install/skillhub.md to install @ixhlink/ixhlink-skills-image-explain.
About this skill
Specific Problem Addressed
In image-driven applications like content moderation, data analysis, or automated workflows, extracting structured information or performing semantic understanding from images is essential. Traditional approaches often require complex preprocessing models, while large models offer end-to-end solutions, but integrating them involves API alignment, payment management, and error handling.
The Smart Drawing Image Understanding skill encapsulates synchronous image analysis capabilities, allowing developers to submit image URLs or Base64 data via a simple interface and receive textual understanding results. This reduces the burden of building visual models from scratch, especially suitable for scenarios requiring rapid integration of image analysis features.
Core Capabilities and Workflow
The skill's core is vision capability, processing image understanding requests synchronously. Key steps include:
- Query Models (optional): Verify model availability via
GET /api/v1/llm/models. - Submit Request: Use
POST /api/v1/llm/invokewith a request body where themessagesarray contains image and text content. Two image input formats are supported:
- URL images: Provide a publichttpslink inimage_url.url.
- Base64 images: Use thedata:image/...;base64,...format. - Handle Payment: If HTTP 402 is returned, invoke the payment interface, then retry with the same JSON body, adding payment-related headers like
WeixinPay-RequiredandX-Payment-Id. - Parse Response: Extract model reply text from
data.choices[0].message.content.
The request body follows OpenAI-compatible format, with payload fields including:
- messages: Conversation array supporting text and image_url types.
- Optional parameters: system, max_tokens, temperature, etc.
Example request (URL image):
{
"model": "ixhlink-skills-image-explain",
"capability": "vision",
"payload": {
"messages": [
{
"role": "user",
"content": [
{ "type": "text", "text": "Describe this image" },
{ "type": "image_url", "image_url": { "url": "https://example.com/image.jpg" } }
]
}
]
}
}
Caveats and Boundaries
- Payment Pre-check: Before calling, ensure the Agent has payment capabilities installed (e.g.,
weixinpay extension), otherwise prompt and terminate. Service pricing is 0.3 yuan per call, requiring user confirmation before payment flow. - Error Handling: Common HTTP errors include:
400: Parameter errors, such as missing model or invalid message format.402: Payment required or payment failure.404: Model not enabled.503: Service unavailable.
For debugging, usedata._billing.call_log_idto check logs.- Synchronous Limitation: The skill is synchronous, suitable for low-latency scenarios; high-volume requests may need queue management.
- Image Formats: Only publicly accessible URLs or Base64 encoded images are supported; local files must be uploaded first.
Use Cases
- E-commerce operations need to batch-analyze product images to automatically generate SEO-friendly description copy for listing publication.
- Content moderation teams must check user-uploaded images for compliance, quickly identifying and flagging potentially violating visual elements.
- Technical documentation engineers require automatic extraction of key information from inserted architecture diagrams or flowcharts to generate accessible alternative text when writing manuals.
- Market analysts extract brand logos, textual content, and sentiment trends from social media advertising images to compile competitive analysis reports.
Best For
- E-commerce operations specialist: Must process hundreds of new product images weekly, seeking to quickly generate accurate and compelling product descriptions.
- Content safety moderator: Reviews thousands of user images daily, seeking efficient identification of violating content to reduce manual re-review workload.
- UX designer: Needs to quickly parse interaction details from screenshots during design reviews, seeking structured feedback to optimize interfaces.
- Data annotation engineer: Requires generating detailed text labels for images in training datasets, seeking improved annotation consistency and efficiency.
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