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Micro-Expression Recognition & Analysis Tool | 微观情绪识别分析工具

Professional discernment of subtle cues! It performs detailed analysis and recognition of facial micro-expressions, outputs precise emotional state reports, and unveils true inner emotional activities. | 微观情绪（微表情）识别分析工具，专业察言观色！针对人物面部微表情进行细致分析识别，输出精准的情绪状态分析报告，揭示真实内心情绪活动 Skill: Micro-Expression Recognition & Analysis Tool | 微观情绪识别分析工具 Owner: smyx-sunjinhui Summary: Professional discernment of subtle cues! It performs detailed analysis and recognition of facial micro-expressions, outputs precise emotional state reports, and unveils true inner emotional activities. | 微观情绪（微表情）识别分析工具，专业察言观色！针对人物面部微表情进行细致分析识别，输出精准的情绪状态分析报告，揭示真实内心情绪活动 Tags: latest:1.0.17 Version history: v1.0.17 | 2026-10

OpenClaw

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Rank

Safety

Downloads

2.2k

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. 2.2K 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: 2.2K downloadsadoption · observed Oct 9, 2026
Latest release: 1.0.17release · observed Oct 2, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17cfv2h26b7hq5w47tgfy23xx83z9x6:smyx-emotion-analysis

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.
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-smyx-sunjinhui-smyx-emotion-analysis/snapshot"

Documentation

CLAWHUB

140,939 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: "emotion-analysis"
description: "Professional discernment of subtle cues! It performs detailed analysis and recognition of facial micro-expressions, outputs precise emotional state reports, and unveils true inner emotional activities. | 微观情绪（微表情）识别分析工具，专业察言观色！针对人物面部微表情进行细致分析识别，输出精准的情绪状态分析报告，揭示真实内心情绪活动"
version: "1.0.16"
license: "MIT-0"
---

# 😊 Micro-Expression Recognition & Analysis Tool | 微观情绪识别分析工具
> **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 | **微观情绪识别分析工具** |
| 🎯 核心目标 | 微观情绪（微表情）识别分析工具，专业察言观色！针对人物面部微表情进行细致分析识别，输出精准的情绪状态分析报告，揭示真实内心情绪活动 |
| 🖼️ 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 | `EMOTION_ANALYSIS` |

This tool is a professional emotion analysis system based on cutting-edge computer vision and psychological
micro-expression theory. It captures facial features with millimeter-level precision and deeply analyzes fleeting,
subtle muscle changes. By accurately identifying basic emotions—such as surprise, fear, anger, and joy—as well as
complex psychological states, the system generates a detailed emotional state analysis report. Designed to penetrate
linguistic and superficial disguises, it objectively reveals an individual's true inner emotional fluctuations,
providing scientific and quantifiable data support for psychological assessment, human-computer interaction, and
behavioral analysis.

本工具是一款基于前沿计算机视觉与心理学微表情理论的专业情绪分析系统，能够对人物面部进行毫米级特征捕捉，深度解析转瞬即逝的细微肌肉变化。通过对惊讶、恐惧、愤怒、愉悦等基础情绪及复杂心理状态的精准识别，系统将生成一份详尽的情绪状态分析报告，旨在穿透语言与表层的伪装，客观揭示个体最真实的内心情感波动，为心理评估、人机交互及行为分析提供科学、量化的数据支撑。

## 🎬 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

通过视频分析对人物面部微表情进行精准识别，获取结构化的真实情绪状态分析报告

### 2. 🛠️ 能力范围

| 序号 | 具体能力 |
|---:|---|
| 1 | 视频分析 |
| 2 | 基础情绪识别 |
| 3 | 复合情绪识别 |
| 4 | 微表情捕捉 |
| 5 | 真实情绪揭示 |
| 6 | 情绪波动分析 |
| 7 | 人际交往情绪建议 |

### 3. ⚡ 触发条件

| 触发类型 | 触发规则 |
|---|---|
| ✅ 默认触发 | **默认触发**：当用户提供需要分析的人物面部视频 URL 或文件需要进行微表情情绪分析时，默认触发本技能 |
| 🔎 明确分析意图 | 当用户明确需要进行微表情分析、情绪识别、察言观色、真实情绪探测时，提及情绪分析、微表情、表情识别、察言观色、真实情绪等关键词，并且上传了视频文件 |
| 📚 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** ：查看历史情绪报告、微表情分析报告清单、情绪分析列表、显示所有情绪报告，查询微表情情绪分析报告 |

### 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.emotion_analysis --list
```

| 类型 | 要求 |
|---|---|
| ✅ 必须 | 使用 `python -m scripts.emotion_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
  ```

## 😊 微表情情绪识别分析维度 | Micro-Expression Dimensions
| 维度/类型 | 说明 |
|---|---|
| **开心/愉悦** | 眼角鱼尾纹、嘴角上扬、面颊抬起 |
| 轻微愉悦 | 礼貌性微笑，肌肉轻微收缩 |
| 中度愉悦 | 自然开心笑，双眼弯起 |
| 重度愉悦 | 开怀大笑，面部肌肉充分舒展 |

_meta.json

{
  "ownerId": "kn7cyxkr2ymf7zwsrybpedg8xx83ywrj",
  "slug": "smyx-emotion-analysis",
  "version": "1.0.17",
  "publishedAt": 1790955145939
}

references/api_doc.md

# API 接口文档

此处用于存放微表情情绪分析 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` - 启动分析任务
2. `/web/ai-analysis/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. `/health/order/api/getReportDetailExport?id={id}` - 导出完整报告

## 场景代码

- `EMOTION_ANALYSIS` - 微观情绪（微表情）识别分析

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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