---
name: linkfox-ruiguan-image-compliance
description: 基于睿观的产品图片政策合规检测，通过视觉相似度匹配识别潜在违规商品。当用户提到政策合规检查、产品图片合规、违规检测、禁售商品筛查、基于图片的合规审查、上架前风险排查、policy compliance detection, product compliance review, violation detection, image compliance check, product image risk screening, Ruiguan时触发此技能。即使用户未明确说"合规"，只要其需求涉及将产品图片与违规数据库进行比对，也应触发此技能。
---

# 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:
```bash
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.

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## 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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