What makes 专业物体识别技能。输入图片，返回场景、物体名称、类别、品牌、材质、价格和置信度 notable?

1.6K downloads. Trust evidence available

How should 专业物体识别技能。输入图片，返回场景、物体名称、类别、品牌、材质、价格和置信度 be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

专业物体识别技能。输入图片，返回场景、物体名称、类别、品牌、材质、价格和置信度

专业物体识别，输入图片返回场景描述及物体名称、类别、品牌、材质、价格区间和识别置信度。 Skill: 专业物体识别技能。输入图片，返回场景、物体名称、类别、品牌、材质、价格和置信度 Owner: by123 Summary: 专业物体识别，输入图片返回场景描述及物体名称、类别、品牌、材质、价格区间和识别置信度。 Tags: latest:1.0.0 Version history: v1.0.0 | 2026-02-24T08:26:27.351Z | auto Initial release of ObjectVision 1.0.0 - Provides professional object recognition from images, returning scene description, object names, categories, brands, materials, estimated prices, and confidence scores. - Supports variable d

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.6k

Updated

Oct 10, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.6K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.6K downloadsadoption · observed Oct 10, 2026
Latest release: 1.0.0release · observed Feb 24, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17e7vwq0a9z93wg5kfpxx16cd885vw5:objects-detection

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-by123-objects-detection/snapshot"

Documentation

CLAWHUB

4,219 characters of source documentation, loaded on request.

Extracted files

2 files captured from the source.

_meta.json

{
  "ownerId": "kn70mjqe7w5mjenvn0zc23h13h803y0t",
  "slug": "objects-detection",
  "version": "1.0.0",
  "publishedAt": 1771921587351
}

skills.md

# OpenClaw Skill + iOS 集成示例：ObjectVision

## 一、Skill 信息

- **Skill 名称**: ObjectVision
- **版本**: 1.0.0
- **类型**: 视觉识别 / 物体识别
- **作者**: YourName
- **描述**: 专业物体识别技能。输入图片，返回场景、物体名称、类别、品牌、材质、价格和置信度。
- **标签**: object recognition, vision, ai, 物体识别
- **License**: MIT

---

## 二、Skill 配置 JSON

```json
{
  "name": "ObjectVision",
  "description": "专业物体识别技能。输入图片，返回场景、物体名称、类别、品牌、材质、价格和置信度。",
  "version": "1.0.0",
  "input_schema": {
    "type": "object",
    "properties": {
      "image_url": {
        "type": "string",
        "description": "要识别的图片 URL"
      },
      "detection_level": {
        "type": "string",
        "enum": ["basic", "full", "professional"],
        "default": "full",
        "description": "识别精度等级，basic: 基础物体, full: 品牌材质, professional: 包含小物件/抽屉内部"
      }
    },
    "required": ["image_url"]
  },
  "output_schema": {
    "type": "object",
    "properties": {
      "scene": {
        "type": "string",
        "description": "图片场景描述，例如卧室桌面、办公室"
      },
      "objects": {
        "type": "array",
        "description": "检测到的物体列表",
        "items": {
          "type": "object",
          "properties": {
            "name": { "type": "string", "description": "物体名称" },
            "category": { "type": "string", "description": "物体类别" },
            "confidence": { "type": "number", "description": "识别置信度，0-1" },
            "bounding_box": {
              "type": "array",
              "description": "[x, y, width, height] 物体框坐标",
              "items": { "type": "number" }
            },
            "brand": { "type": "string", "description": "品牌，可推理" },
            "material": { "type": "string", "description": "材质，可推理" },
            "estimated_price_range": { "type": "string", "description": "估算价格区间" }
          },
          "required": ["name", "category", "confidence", "bounding_box"]
        }
      }
    },
    "required": ["scene", "objects"]
  },
  "prompt": "You are a professional object recognition engine. Analyze the image provided in 'image_url' and return a JSON with the following: 1. Detect all objects. 2. For each object output: name, category, confidence (0-1), bounding_box [x, y, width, height], brand (if identifiable), material (if inferable), estimated_price_range (if known). 3. Provide scene description. 4. Return only JSON, no explanations. 5. If uncertain, lower confidence score instead of hallucinating.",
  "examples": [
    {
      "input": {
        "image_url": "https://example.com/photo.jpg",
        "detection_level": "full"
      },
      "output": {
        "scene": "office desk",
        "objects": [
          {
            "name": "iPhone 15 Pro",
            "category": "Electronics",
            "confidence": 0.95,
            "bounding_box": [100, 50, 200, 400],
            "brand": "Apple",
            "material": "Titanium",
            "estimated_price_range": "$999-$1299"
          },
          {
            "name": "MacBook Pro 16",
            "category": "Electronics",
            "confidence":

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