linkfox-ruiguan-image-compliance

Ruiguan Policy Compliance Image Detection

This skill guides you on how to use the Ruiguan policy compliance detection tool to identify potential policy violations in product images. It performs image-based similarity search against a known database of prohibited products.

Core Concepts

Ruiguan Policy Compliance Image Detection is an image-based compliance screening service. Given a product image URL, it searches for visually similar products in a database of known policy-violating items. The tool returns matching violations ranked by visual similarity.

Similarity score (cosine): A value between 0 and 1. Higher values indicate stronger visual resemblance to known violating products. A score close to 1.0 means the product image is nearly identical to a flagged violation.

Parameter Guide

Parameter	Type	Required	Description	Example
Image URL	imageUrl	Yes	The URL of the product image to check (max 1000 chars)	https://example.com/product.jpg

Key notes:

• The image URL must be publicly accessible
• Supported formats include common image types (JPG, PNG, etc.)
• The URL must not exceed 1000 characters

Response Fields

Field	API Name	Description
Total Matches	total	Number of matching violation records found
Violation List	data	Array of matched violating products
Violation Image	pdImgOssUrl	Image URL of the matched violating product
Similarity Score	cosine	Similarity between the input image and the violation (0~1)
Product Title (EN)	pdTitle	English title of the matched violating product
Product Title (CN)	pdTitleCHNCensored	Chinese title of the matched violating product
Detection ID	detectId	Unique identifier for this detection session
Token Cost	costToken	Number of tokens consumed by this request

Local Image Upload

This tool requires a publicly accessible image URL. If the user provides a local image file path (e.g., C:\Users\...\photo.png, /home/.../image.jpg), you must upload it first to obtain a public URL.

Run the upload script:

python scripts/upload_image.py /path/to/local/image.png

The script will return a public URL (valid for 24 hours) that can be used as the image URL parameter.

Usage Examples

1. Basic compliance check for a single product image

Check this product image for policy compliance: https://example.com/images/product-123.jpg

2. Batch checking multiple product images

Please scan these product images for potential policy violations:
- https://example.com/images/item-a.jpg
- https://example.com/images/item-b.jpg

3. Pre-listing compliance screening

Before I list this product, can you check if the image triggers any policy flags?
Image: https://example.com/new-product.png

Display Rules

1. Show results in a clear table: Present each matched violation with its image, similarity score, and product titles
2. Highlight high-similarity matches: When the cosine score exceeds 0.8, clearly flag the result as a strong match that likely requires attention
3. Include violation images: When results contain pdImgOssUrl, display the matched violation image so the user can visually compare
4. Score interpretation: Always explain what the similarity score means -- higher values indicate closer resemblance to known violations
5. Error handling: When a query fails, explain the issue and suggest checking whether the image URL is valid and publicly accessible
6. No legal advice: Present detection results factually without providing legal conclusions; remind users to verify with platform policies

Important Limitations

• Image-only detection: This tool works exclusively with image URLs; it does not analyze text descriptions or product metadata
• URL accessibility: The image URL must be publicly reachable by the detection service
• URL length cap: Image URLs must not exceed 1000 characters
• Similarity-based: Results are based on visual similarity and do not constitute a definitive policy ruling

User Expression & Scenario Quick Reference

Applicable -- Image-based product policy compliance checks:

User Says	Scenario
"Check if this product image has compliance risks"	Single image compliance check
"Scan my product images for policy violations"	Batch compliance screening
"Is this image flagged as a prohibited product"	Specific violation inquiry
"Pre-screen my listing images for policy risks"	Pre-listing compliance audit
"Find similar violations for this product image"	Similarity-based violation search
"这个产品能安全上架吗"	合规风险预检
"帮我检测一下这个图片是否违规"	单图合规检测

Not applicable -- Needs beyond image-based policy compliance detection:

• Text-based product compliance analysis
• General product category classification
• Intellectual property / trademark infringement
• Patent or copyright detection (use other Ruiguan skills)

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply:

1. The functionality or purpose described in this skill does not match actual behavior
2. The skill's results do not match the user's intent
3. The user expresses dissatisfaction or praise about this skill
4. Anything you believe could be improved

Call the feedback API as specified in references/api.md. Do not interrupt the user's flow.

Handling Large Responses

To avoid overflowing the agent context, persist the response to disk and extract only the fields you need:

python scripts/response_io.py run --script scripts/ruiguan_image_compliance_search.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>"   # or --path "<JMESPath>"

Pick --out-dir outside any git working tree (e.g. /tmp/... on Unix, %TEMP%/... on Windows). Persisted responses may contain PII, pricing, or auth-sensitive data — do not commit them. Files are not auto-deleted; clean up when the task is done.

This skill exposes multiple entry scripts: ruiguan_image_compliance_search.py, upload_image.py. Pass --script scripts/<name>.py to choose the one you need.

run writes the full response to a file and emits only a schema preview + file path. read projects specific fields, with --limit/--offset for slicing and --format json|jsonl|csv|table for output.

When to prefer this pattern — apply your judgment based on the response characteristics, e.g.:

• High field count per record, or fields you don't need
• Batch/paginated results (multiple items per call)
• Long-text fields (descriptions, reviews, HTML, time series)
• Output reused across later steps rather than consumed immediately

For small, single-use responses, calling the main script directly is fine.

⚠️ The preview is a truncated schema + sample, not the full data. Any field-level decision must read from the persisted file via read.

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