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Visual Summarization Skill | 视觉摘要智述技能

Performs AI analysis on input video clips/image content and generates a smooth, natural scene description. | 视觉摘要智述技能，对传入的视频片段/图片内容进行AI分析，生成一段通顺自然的场景描述内容 Skill: Visual Summarization Skill | 视觉摘要智述技能 Owner: 18072937735 Summary: Performs AI analysis on input video clips/image content and generates a smooth, natural scene description. | 视觉摘要智述技能，对传入的视频片段/图片内容进行AI分析，生成一段通顺自然的场景描述内容 Tags: latest:1.0.17 Version history: v1.0.17 | 2026-10-03T22:23:26.880Z | auto - Bumped version to 1.0.18. - Updated SKILL.md with latest information and minor clarifications. - Removed redunda

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

2.4k

Updated

Oct 9, 2026

Version

1.0.17

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 2.4K downloads reported by the source. Last updated 10/9/2026.

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 9, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 9, 2026
Adoption signal
2.4K downloadsadoption · observed Oct 9, 2026
Latest release
1.0.17release · observed Oct 3, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17f8q65zg3y98t86jdg1177g583whq8:smyx-visual-summary-analysis
  1. Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
  2. 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-18072937735-smyx-visual-summary-analysis/snapshot"

Documentation

CLAWHUB

121,259 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: "visual-summary-analysis"
description: "Performs AI analysis on input video clips/image content and generates a smooth, natural scene description. | 视觉摘要智述技能，对传入的视频片段/图片内容进行AI分析，生成一段通顺自然的场景描述内容"
version: "1.0.18"
license: "MIT-0"
---

# 📝 Visual Summarization Skill | 视觉摘要智述技能
> **智能分析中枢** · 图片/视频智能分析 · 结构化报告 · 历史报告云端查询

---

## 🧭 技能概览 | Overview

| 模块 | 内容 |
|---|---|
| 🏷️ 技能名称 | **视觉摘要智述技能** |
| 🎯 核心目标 | 视觉摘要智述技能，对传入的视频片段/图片内容进行AI分析，生成一段通顺自然的场景描述内容 |
| 🖼️ 输入类型 | 图片、视频、本地文件、网络 URL |
| 📝 输出能力 | 结构化分析报告、识别/监测结果、建议与报告链接 |
| 🧩 场景码 | `VISUAL_SUMMARY` |

Based on advanced multimodal large models and video understanding technologies, this feature performs deep semantic
analysis and logical reasoning on input video clips or images. Utilizing computer vision algorithms, the system
precisely identifies key visual elements—including subject objects, environmental backgrounds, action behaviors, and
lighting atmosphere. It then combines this with Natural Language Generation (NLG) technology to transform abstract
visual information into smooth, logically coherent scene descriptions. Whether dealing with dynamic video events or
static image moments, the system captures critical details and restores the on-site context with vivid language. This
provides intelligent text summarization services for scenarios such as video content understanding, accessibility
assistance, and media asset management.

本功能基于先进的多模态大模型与视频理解技术，能够对传入的视频片段或图片进行深度语义分析与逻辑推理。系统通过计算机视觉算法精准识别画面中的主体对象、环境背景、动作行为及光影氛围，并结合自然语言生成技术，将抽象的视觉信息转化为一段通顺自然、逻辑连贯的场景描述。无论是动态的视频事件还是静态的图像瞬间，系统都能捕捉关键细节，用生动的语言还原现场情境，为视频内容理解、无障碍辅助、媒体资产管理等场景提供智能化的文本摘要服务

## 🎬 技能演示 | Skill Demo

[▶️ 点击查看技能使用介绍](https://lifeemergence.com/sample.html)

---

## 🎯 任务目标 | Goals

### 1. 🧩 技能用途

对传入的视频片段或图片内容进行AI分析，自动生成通顺自然的场景描述摘要

### 2. 🛠️ 能力范围

| 序号 | 具体能力 |
|---:|---|
| 1 | 场景内容识别 |
| 2 | 物体识别 |
| 3 | 行为识别 |
| 4 | 文字提取 |
| 5 | 整合成一段流畅自然的中文描述 |

### 3. ⚡ 触发条件

| 触发类型 | 触发规则 |
|---|---|
| ✅ 默认触发 | **默认触发**：当用户提供视频/图片需要生成内容描述/视觉摘要时，默认触发本技能 |
| 🔎 明确分析意图 | 当用户明确需要视频内容描述、图片内容摘要、视觉智述时，提及视频摘要、内容描述、视觉摘要智述、视频转文字等关键词，并且上传了视频/图片 |
| 📚 历史报告查询 | 当用户提及以下关键词时，**自动触发历史报告查询功能** ：查看历史摘要报告、摘要报告清单、报告列表、查询历史摘要报告、显示所有摘要报告、视觉智述分析报告，查询视觉摘要智述分析报告 |

### 4. 🤖 自动行为

| 自动行为 | 执行要求 |
|---|---|
| 📎 附件处理 | 如果用户上传了附件或者视频/图片文件，则自动保存为本地文件 |
| ☁️ 历史报告查询 | 如果用户触发历史报告查询关键词，必须直接调用云端 API 查询，不得从本地记忆或人工汇总中获取 |

#### ⚠️ 强制数据获取规则（次高优先级）

> **橙色强约束：** 历史报告清单只允许从云端接口读取，不允许从本地记录、长期记忆或人工汇总中提取。

必须执行：

```bash
python -m scripts.visual_summary_analysis --list
```

| 类型 | 要求 |
|---|---|
| ✅ 必须 | 使用 `python -m scripts.visual_summary_analysis --list` 调用 API 查询云端的历史报告数据 |
| 🚫 严格禁止 | 从本地 `memory` 目录读取历史会话信息 |
| 🚫 严格禁止 | 手动汇总本地记录中的报告 |
| 🚫 严格禁止 | 从长期记忆中提取报告 |
| ✅ 输出格式 | 必须统一从云端接口获取最新完整数据，然后以 Markdown 表格格式输出结果 |

## 📦 前置准备 | Requirements
- 依赖说明:scripts 脚本所需的依赖包及版本
  ```
  requests>=2.28.0
  ```

## 📸 使用要求 | Usage Requirements
| 要求项 | 说明 |
|---|---|
| 视频/图片内容清晰 | ，主要物体和场景完整可见 |
| 视频片段长度建议不超过 5 分钟，过长内容建议分段描述 | 视频片段长度建议不超过 5 分钟，过长内容建议分段描述 |
| 主要场景主体不被大面积遮挡 | 主要场景主体

_meta.json

{
  "ownerId": "kn7e2caqj7pnsvr9r7t8zenghs83xw7n",
  "slug": "smyx-visual-summary-analysis",
  "version": "1.0.17",
  "publishedAt": 1791066206880
}

references/api_doc.md

# API 接口文档

此处用于存放视觉摘要智述分析 API 的接口文档，待后续补充。

## 接口规范

- 基础地址：由 smyx_common 配置统一管理
- 认证方式：API Key 鉴权
- 请求格式：支持文件上传
- 响应格式：JSON

## 主要接口

1. `/web/ai-analysis/v2/start-common-ai-analysis` - 启动AI分析任务
2. `/web/ai-analysis/v2/get-common-ai-analysis-result` - 获取分析结果
3. `/web/ai-analysis/page-common-ai-analysis-result` - 分页查询历史报告
4. `/ai/order/api/getReportDetailExport?id={id}` - 导出完整报告

## 场景代码

- `OPEN_VISUAL_SUMMARY_ANALYSIS` - 开放平台视觉摘要智述分析

skills/smyx_analysis/references/api_doc.md

# API接口文档

## 接口规范

- 基础地址：由 smyx_common 配置统一管理
- 认证方式：API Key 鉴权
- 请求格式：支持文件上传
- 响应格式：JSON

## 错误码说明

| 错误码 | 说明       |
|-----|----------|
| 400 | 请求参数错误   |
| 401 | API密钥无效  |
| 403 | 权限不足     |
| 413 | 文件过大     |
| 415 | 不支持的文件格式 |
| 500 | 服务器内部错误  |

scripts/config.yaml

{}

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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Record generated Oct 9, 2026.

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