feat: 儿童心理陪伴筛查插件初始版本

- 核心 PsychoScreener 模块，支持 MiniMax API 调用
- 8 种心理问题类别检测（霸凌、抑郁、焦虑、家庭矛盾等）
- ScreeningResult 数据模型，含类别/严重程度/建议
- 单元测试 12 个（含参数化测试，覆盖 7 个虚构场景）
- build_response_prefix() 支持检测后注入前缀标记
- pyproject.toml + .gitignore 完整项目脚手架

.env.example

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+# 儿童心理陪伴玩偶 - 环境变量配置
+# 复制此文件为 .env 并填入实际值
+
+# MiniMax API Key（必填）
+# 获取地址：https://platform.minimaxi.com/
+MINIMAX_API_KEY=your-api-key-here
+
+# MiniMax 模型（可选，默认 MiniMax-M2.5）
+MINIMAX_MODEL=MiniMax-M2.5
+
+# MiniMax API 地址（可选，默认使用官方接口）
+MINIMAX_BASE_URL=https://api.minimaxi.com/v1/text/chatcompletion_v2
+
+# 日志级别
+LOG_LEVEL=INFO

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*.so
+.venv/
+venv/
+.env
+
+# Test
+.pytest_cache/
+.coverage
+htmlcov/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+
+# Build
+*.egg-info/
+dist/
+build/
+
+# OS
+.DS_Store

README.md

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+# 儿童心理陪伴玩偶
+
+基于小智AI生态的儿童心理筛查插件，通过分析儿童与玩偶的对话内容，
+识别潜在的心理问题（如霸凌、抑郁情绪、焦虑、家庭矛盾等），
+为家长提供早期预警。
+
+## 项目结构
+
+```
+child-psycho-companion/
+├── src/
+│   └── psycho_screener/       # 核心筛查模块
+│       ├── __init__.py
+│       └── screener.py       # 筛查器实现
+├── tests/
+│   ├── __init__.py
+│   ├── conftest.py
+│   └── test_screener.py     # 单元测试
+├── .env.example              # 环境变量模板
+├── pyproject.toml
+└── README.md
+```
+
+## 快速开始
+
+### 1. 安装
+
+```bash
+cd child-psycho-companion
+pip install -e .
+```
+
+### 2. 配置 API Key
+
+```bash
+export MINIMAX_API_KEY=your-api-key-here
+```
+
+### 3. 使用示例
+
+```python
+from psycho_screener import PsychoScreener
+
+screener = PsychoScreener(api_key="your-api-key")
+
+# 对儿童对话进行筛查
+context = """
+孩子：今天在学校，小明又打我了，我好害怕。
+孩子：他说如果我告诉老师就会打我。
+"""
+result = screener.screen(context)
+
+if result.detected:
+    print(f"检测到问题：{result.summary}")
+    prefix = screener.build_response_prefix(result)
+    print(f"响应前缀：{prefix}")
+```
+
+### 4. 运行测试
+
+```bash
+# 安装测试依赖
+pip install -e ".[dev]"
+
+# 运行单元测试（Mock 模式，不调用真实 API）
+pytest tests/test_screener.py -v -m unit
+
+# 运行集成测试（需要真实 API key）
+export MINIMAX_API_KEY=your-key
+pytest tests/test_screener.py -v -m integration
+```
+
+## 核心流程
+
+```
+儿童语音 → 小智AI (STT) → 对话上下文
+                                ↓
+                    心理筛查器 (MiniMax API)
+                                ↓
+                    ScreeningResult {detected, category, severity}
+                                ↓
+                    ┌───────────┴───────────┐
+                 detected=True          detected=False
+                      ↓                      ↓
+              注入前缀标记              原样返回
+         "已发现特定心理问题：..."
+```
+
+## 检测类别
+
+| 类别 | 描述 | 严重程度 |
+|------|------|---------|
+| bullying | 霸凌/同伴冲突 | low-high |
+| depression | 抑郁情绪 | medium-high |
+| anxiety | 焦虑/恐惧 | low-medium |
+| family_conflict | 家庭矛盾 | medium-high |
+| self_esteem | 自卑/自我否定 | low-medium |
+| trauma | 创伤事件 | medium-high |
+| social_isolation | 社交孤立 | medium-high |
+| other | 其他心理需求 | - |
+
+## 下一步
+
+- [ ] 接入 xinnan-tech/xiaozhi-esp32-server MCP 接入点
+- [ ] 构建案例库系统
+- [ ] 开发咨询师终端
+- [ ] 家长端报告界面

pyproject.toml

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+[project]
+name = "child-psycho-companion"
+version = "0.1.0"
+description = "儿童心理陪伴玩偶 - 对话心理筛查插件"
+requires-python = ">=3.10"
+dependencies = [
+    "requests>=2.31.0",
+    "pydantic>=2.0",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=8.0",
+    "pytest-asyncio>=0.23",
+    "pytest-mock>=3.14",
+    "requests-mock>=1.12",
+]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+markers = [
+    "unit: Unit tests with mocked API calls",
+    "integration: Integration tests requiring real API calls",
+]
+
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]

src/psycho_screener/__init__.py

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+"""
+儿童心理陪伴 - 核心筛查模块
+功能：对儿童对话上下文进行心理问题筛查，检测潜在心理需求或问题
+目标年龄段：3-8岁
+"""
+
+from .screener import PsychoScreener, ScreeningResult
+
+__all__ = ["PsychoScreener", "ScreeningResult"]

src/psycho_screener/screener.py

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+"""
+儿童心理陪伴 - 筛查器
+基于 MiniMax API，对儿童对话进行心理问题筛查
+"""
+
+from __future__ import annotations
+
+import os
+import json
+import requests
+from typing import Literal
+from pydantic import BaseModel, Field
+
+
+# ============================================================================
+# 数据模型
+# ============================================================================
+
+class ConcernCategory(str):
+    """心理问题类别"""
+    NONE = "none"
+    BULLYING = "bullying"          # 校园霸凌/同伴冲突
+    DEPRESSION = "depression"       # 抑郁情绪
+    ANXIETY = "anxiety"             # 焦虑/恐惧
+    FAMILY_CONFLICT = "family_conflict"  # 家庭矛盾
+    SELF_ESTEEM = "self_esteem"    # 自卑/自我否定
+    TRAUMA = "trauma"               # 创伤事件
+    SOCIAL_ISOLATION = "social_isolation"  # 社交孤立
+    OTHER = "other"                 # 其他
+
+
+class ScreeningResult(BaseModel):
+    """筛查结果"""
+    detected: bool = Field(description="是否检测到心理问题")
+    category: str = Field(default=ConcernCategory.NONE, description="问题类别")
+    severity: Literal["none", "low", "medium", "high"] = Field(
+        default="none", description="严重程度"
+    )
+    summary: str = Field(default="", description="简要描述检测到的问题")
+    suggestion: str = Field(default="", description="建议行动")
+    raw_response: str = Field(default="", description="模型原始响应（调试用）")
+
+
+# ============================================================================
+# 筛查系统提示词
+# ============================================================================
+
+SYSTEM_PROMPT = """你是一个专业的儿童心理咨询师助手，专注于分析3-8岁儿童的对话内容，识别潜在的心理需求或问题。
+
+## 你的任务
+分析给定的儿童对话上下文，判断是否存在以下心理问题类别：
+
+1. **bullying** - 霸凌/同伴冲突：孩子表达被欺负、被嘲笑、被孤立、被人威胁等
+2. **depression** - 抑郁情绪：孩子表达悲伤、绝望、无助、对事物失去兴趣、提到"不想活了"等
+3. **anxiety** - 焦虑/恐惧：孩子表达担心、害怕、做噩梦、回避某些情境等
+4. **family_conflict** - 家庭矛盾：孩子表达父母争吵、离婚担心、被忽视、被严厉惩罚等
+5. **self_esteem** - 自卑/自我否定：孩子表达"我不行"、"没人喜欢我"、"我太笨了"等
+6. **trauma** - 创伤事件：孩子描述意外事故、暴力事件、亲人离世等创伤性经历
+7. **social_isolation** - 社交孤立：孩子表达没有朋友、被排斥、孤独感等
+8. **other** - 其他值得关注的心理需求
+
+## 输出格式
+请严格按以下JSON格式返回（不要添加任何额外内容）：
+{
+  "detected": true/false,
+  "category": "具体类别",
+  "severity": "none/low/medium/high",
+  "summary": "一句话描述检测到的问题",
+  "suggestion": "建议的应对方式（简短，1-2句话）"
+}
+
+## 判断标准
+- **low**: 轻微迹象，需要关注但无需立即介入
+- **medium**: 中度迹象，建议与家长沟通
+- **high**: 严重迹象，需要专业干预
+
+如果对话内容完全正常，没有任何心理问题迹象，返回：
+{
+  "detected": false,
+  "category": "none",
+  "severity": "none",
+  "summary": "未检测到心理问题",
+  "suggestion": ""
+}
+
+注意：
+- 只关注确实存在问题的迹象，不要过度解读
+- 儿童的语言表达可能不精确，需要结合上下文判断
+- 正常的情绪表达（偶尔哭、发脾气）不构成问题"""
+
+
+# ============================================================================
+# 筛查器
+# ============================================================================
+
+class PsychoScreener:
+    """
+    儿童心理问题筛查器
+
+    使用方法：
+        screener = PsychoScreener(api_key="your-api-key")
+        result = screener.screen("今天小明打我了，我很伤心")
+        if result.detected:
+            print(f"检测到问题：{result.summary}")
+    """
+
+    def __init__(
+        self,
+        api_key: str | None = None,
+        model: str = "MiniMax-M2.5",
+        base_url: str = "https://api.minimaxi.com/v1/text/chatcompletion_v2",
+    ):
+        self.api_key = api_key or os.environ.get("MINIMAX_API_KEY", "")
+        self.model = model
+        self.base_url = base_url
+
+        if not self.api_key:
+            raise ValueError(
+                "MiniMax API key is required. "
+                "Set MINIMAX_API_KEY env var or pass api_key parameter."
+            )
+
+    def _call_minimax(self, messages: list[dict]) -> str:
+        """调用 MiniMax API"""
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+
+        payload = {
+            "model": self.model,
+            "messages": messages,
+        }
+
+        response = requests.post(
+            self.base_url,
+            headers=headers,
+            json=payload,
+            timeout=30,
+        )
+        response.raise_for_status()
+        data = response.json()
+
+        # 兼容不同返回格式
+        if "choices" in data:
+            return data["choices"][0]["message"]["content"]
+        elif "output" in data:
+            return data["output"]
+        return str(data)
+
+    def screen(self, context: str) -> ScreeningResult:
+        """
+        对给定的对话上下文进行心理问题筛查
+
+        Args:
+            context: 儿童的对话上下文（可以是多轮对话的汇总文本）
+
+        Returns:
+            ScreeningResult: 包含检测结果的数据模型
+        """
+        messages = [
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {"role": "user", "content": f"请分析以下儿童对话内容：\n\n{context}"},
+        ]
+
+        try:
+            raw_response = self._call_minimax(messages)
+        except Exception as e:
+            return ScreeningResult(
+                detected=False,
+                category=ConcernCategory.OTHER,
+                severity="none",
+                summary=f"API调用失败: {str(e)}",
+                suggestion="",
+                raw_response=str(e),
+            )
+
+        # 尝试解析 JSON
+        try:
+            # 提取 JSON（可能模型返回带有 markdown 代码块）
+            content = raw_response.strip()
+            if content.startswith("```"):
+                lines = content.split("\n")
+                content = "\n".join(lines[1:-1])  # 去掉 ```json 和 ```
+            
+            parsed = json.loads(content)
+            return ScreeningResult(
+                detected=parsed.get("detected", False),
+                category=parsed.get("category", ConcernCategory.NONE),
+                severity=parsed.get("severity", "none"),
+                summary=parsed.get("summary", ""),
+                suggestion=parsed.get("suggestion", ""),
+                raw_response=raw_response,
+            )
+        except json.JSONDecodeError:
+            # 无法解析 JSON，返回原始内容
+            return ScreeningResult(
+                detected=False,
+                category=ConcernCategory.OTHER,
+                severity="none",
+                summary="无法解析模型响应",
+                suggestion="",
+                raw_response=raw_response,
+            )
+
+    def build_response_prefix(self, result: ScreeningResult) -> str:
+        """
+        根据筛查结果构建响应前缀
+
+        Args:
+            result: 筛查结果
+
+        Returns:
+            str: 如果检测到问题，返回前缀字符串；否则返回空字符串
+        """
+        if not result.detected:
+            return ""
+        
+        return f"【已发现特定心理问题】类别：{result.category}，严重程度：{result.severity}，描述：{result.summary}"

tests/conftest.py

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+# Test configuration for pytest

tests/test_screener.py

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+"""
+单元测试 - 儿童心理筛查器
+
+测试场景：使用虚构的儿童对话上下文，验证筛查函数能否正确识别并标注心理问题
+
+运行方式：
+    pytest tests/test_screener.py -v
+"""
+
+import pytest
+import json
+from unittest.mock import patch, MagicMock
+from psycho_screener.screener import (
+    PsychoScreener,
+    ScreeningResult,
+    ConcernCategory,
+)
+
+
+# ============================================================================
+# Mock API 响应（用于测试）
+# ============================================================================
+
+MOCK_RESPONSES = {
+    # 场景1：霸凌/同伴冲突
+    "bullying": json.dumps({
+        "detected": True,
+        "category": "bullying",
+        "severity": "medium",
+        "summary": "孩子描述被同学欺负、被嘲笑，感到伤心和无助",
+        "suggestion": "建议家长与孩子耐心沟通，了解具体情况，并与学校老师联系"
+    }),
+    
+    # 场景2：抑郁情绪
+    "depression": json.dumps({
+        "detected": True,
+        "category": "depression",
+        "severity": "high",
+        "summary": "孩子表达对生活失去兴趣、经常哭泣、觉得自己没有价值",
+        "suggestion": "高度建议寻求专业儿童心理咨询师帮助，密切关注孩子的安全"
+    }),
+    
+    # 场景3：焦虑/恐惧
+    "anxiety": json.dumps({
+        "detected": True,
+        "category": "anxiety",
+        "severity": "low",
+        "summary": "孩子表达对即将到来的考试感到紧张，有些担心",
+        "suggestion": "可以通过游戏和放松训练帮助孩子缓解焦虑情绪"
+    }),
+    
+    # 场景4：正常对话（无问题）
+    "normal": json.dumps({
+        "detected": False,
+        "category": "none",
+        "severity": "none",
+        "summary": "未检测到心理问题",
+        "suggestion": ""
+    }),
+    
+    # 场景5：家庭矛盾
+    "family_conflict": json.dumps({
+        "detected": True,
+        "category": "family_conflict",
+        "severity": "high",
+        "summary": "孩子描述父母经常争吵、提到害怕父母离婚",
+        "suggestion": "建议家长注意家庭氛围对孩子的影响，考虑家庭咨询"
+    }),
+    
+    # 场景6：自卑/自我否定
+    "self_esteem": json.dumps({
+        "detected": True,
+        "category": "self_esteem",
+        "severity": "medium",
+        "summary": "孩子反复说自己很笨、什么都不如别人、不值得被喜欢",
+        "suggestion": "建议多给予孩子正面鼓励和肯定，避免过度批评"
+    }),
+    
+    # 场景7：社交孤立
+    "social_isolation": json.dumps({
+        "detected": True,
+        "category": "social_isolation",
+        "severity": "medium",
+        "summary": "孩子表达在学校没有朋友、午餐时间独自一人、感到孤独",
+        "suggestion": "建议帮助孩子学习社交技巧，与学校合作创造更多交友机会"
+    }),
+}
+
+
+# ============================================================================
+# 测试用例：虚构儿童对话上下文
+# ============================================================================
+
+TEST_CASES = [
+    # ---- 霸凌/同伴冲突 ----
+    {
+        "case_id": "bullying_01",
+        "scenario": "同伴冲突",
+        "context": (
+            "孩子：今天在学校，小明又打我了，他抢了我的玩具还不让我告诉老师，"
+            "他说如果我告诉老师就会打我。我很害怕，我现在不想去学校了。\n"
+            "孩子：每次下课我都躲起来，因为我怕再被他欺负。\n"
+        ),
+        "expected_detected": True,
+        "expected_category": "bullying",
+        "expected_severity_min": "medium",  # minimum: at least medium
+    },
+    
+    # ---- 抑郁情绪 ----
+    {
+        "case_id": "depression_01",
+        "scenario": "抑郁情绪",
+        "context": (
+            "孩子：最近我总是觉得很累，什么都不想做，连我最喜欢的动画片都不想看了。\n"
+            "孩子：我觉得我是一个很糟糕的人，没有人喜欢我。\n"
+            "孩子：有时候我想，要是我不在了就好了。\n"
+        ),
+        "expected_detected": True,
+        "expected_category": "depression",
+        "expected_severity_min": "high",
+    },
+    
+    # ---- 焦虑/恐惧 ----
+    {
+        "case_id": "anxiety_01",
+        "scenario": "分离焦虑",
+        "context": (
+            "孩子：明天妈妈要送我上幼儿园，我好害怕，我不想和妈妈分开。\n"
+            "孩子：我担心在幼儿园里会做噩梦，害怕一个人睡觉。\n"
+        ),
+        "expected_detected": True,
+        "expected_category": "anxiety",
+        "expected_severity_min": "low",
+    },
+    
+    # ---- 正常对话 ----
+    {
+        "case_id": "normal_01",
+        "scenario": "正常对话",
+        "context": (
+            "孩子：今天我画了一幅画，是一只大恐龙！\n"
+            "孩子：晚上吃的是我最喜欢的红烧肉，好开心呀！\n"
+            "孩子：明天我想和好朋友一起去公园玩。\n"
+        ),
+        "expected_detected": False,
+        "expected_category": "none",
+        "expected_severity_min": "none",
+    },
+    
+    # ---- 家庭矛盾 ----
+    {
+        "case_id": "family_conflict_01",
+        "scenario": "家庭矛盾",
+        "context": (
+            "孩子：昨天晚上爸爸妈妈又吵架了，吵得很凶，妈妈哭了。\n"
+            "孩子：我很害怕，怕他们会离婚。\n"
+            "孩子：我觉得是我的错，是因为我表现不好他们才吵架的。\n"
+        ),
+        "expected_detected": True,
+        "expected_category": "family_conflict",
+        "expected_severity_min": "high",
+    },
+    
+    # ---- 自卑/自我否定 ----
+    {
+        "case_id": "self_esteem_01",
+        "scenario": "自卑/自我否定",
+        "context": (
+            "孩子：今天老师表扬了小红但是没有表扬我，我就是个笨蛋，什么都做不好。\n"
+            "孩子：班上的同学都不喜欢我，没有人想和我坐同桌。\n"
+            "孩子：我什么都学不会，我好笨啊。\n"
+        ),
+        "expected_detected": True,
+        "expected_category": "self_esteem",
+        "expected_severity_min": "medium",
+    },
+    
+    # ---- 社交孤立 ----
+    {
+        "case_id": "social_isolation_01",
+        "scenario": "社交孤立",
+        "context": (
+            "孩子：今天课间的时候我一个人蹲在角落，没有人来和我玩。\n"
+            "孩子：同学们都有自己的朋友，只有我是一个人。\n"
+            "孩子：我不想去学校了，那里好孤单。\n"
+        ),
+        "expected_detected": True,
+        "expected_category": "social_isolation",
+        "expected_severity_min": "medium",
+    },
+]
+
+
+# ============================================================================
+# Fixtures
+# ============================================================================
+
+@pytest.fixture
+def api_key():
+    """测试用 API key（不会被真实调用）"""
+    return "test-minimax-api-key-12345"
+
+
+@pytest.fixture
+def screener(api_key):
+    """创建筛查器实例"""
+    return PsychoScreener(api_key=api_key)
+
+
+# ============================================================================
+# 测试：真实调用 MiniMax API（需要有效的 API key）
+# ============================================================================
+
+@pytest.mark.integration
+class TestPsychoScreenerIntegration:
+    """集成测试：真实调用 MiniMax API（需设置 MINIMAX_API_KEY）"""
+
+    @pytest.fixture(autouse=True)
+    def check_api_key(self):
+        api_key = os.environ.get("MINIMAX_API_KEY", "")
+        if not api_key:
+            pytest.skip("MINIMAX_API_KEY not set, skipping integration test")
+    
+    def test_screen_bullying(self):
+        api_key = os.environ.get("MINIMAX_API_KEY")
+        screener = PsychoScreener(api_key=api_key)
+        
+        context = TEST_CASES[0]["context"]
+        result = screener.screen(context)
+        
+        print(f"\n[integration] bullying result: {result.model_dump_json(indent=2)}")
+        assert result.detected is True
+        assert result.category == "bullying"
+    
+    def test_screen_normal(self):
+        api_key = os.environ.get("MINIMAX_API_KEY")
+        screener = PsychoScreener(api_key=api_key)
+        
+        context = TEST_CASES[3]["context"]
+        result = screener.screen(context)
+        
+        print(f"\n[integration] normal result: {result.model_dump_json(indent=2)}")
+        assert result.detected is False
+
+
+# ============================================================================
+# 测试：Mock 测试（不真实调用 API）
+# ============================================================================
+
+@pytest.mark.unit
+class TestPsychoScreenerUnit:
+    """单元测试：使用 Mock API 响应"""
+
+    def _mock_api_call(self, case_id: str) -> MagicMock:
+        """根据 case_id 返回对应的 mock 响应"""
+        # 从 case_id 提取场景 key
+        scenario_map = {
+            "bullying": "bullying",
+            "depression": "depression",
+            "anxiety": "anxiety",
+            "normal": "normal",
+            "family_conflict": "family_conflict",
+            "self_esteem": "self_esteem",
+            "social_isolation": "social_isolation",
+        }
+        
+        for key, scenario in scenario_map.items():
+            if key in case_id:
+                mock_resp = MagicMock()
+                mock_resp.raise_for_status = MagicMock()
+                mock_resp.json.return_value = {
+                    "choices": [{"message": {"content": MOCK_RESPONSES[scenario]}}]
+                }
+                return mock_resp
+        
+        # 默认返回 normal
+        mock_resp = MagicMock()
+        mock_resp.raise_for_status = MagicMock()
+        mock_resp.json.return_value = {
+            "choices": [{"message": {"content": MOCK_RESPONSES["normal"]}}]
+        }
+        return mock_resp
+
+    @pytest.mark.parametrize("test_case", TEST_CASES, ids=lambda tc: tc["case_id"])
+    def test_screen_cases(self, api_key, test_case, requests_mock):
+        """参数化测试所有场景"""
+        case_id = test_case["case_id"]
+        
+        # Mock HTTP POST
+        requests_mock.post(
+            "https://api.minimaxi.com/v1/text/chatcompletion_v2",
+            json=self._mock_api_call(case_id).json(),
+        )
+        
+        screener = PsychoScreener(api_key=api_key)
+        result = screener.screen(test_case["context"])
+        
+        # 打印测试结果（方便调试）
+        print(f"\n[test] {case_id} ({test_case['scenario']})")
+        print(f"  detected={result.detected}, category={result.category}, severity={result.severity}")
+        print(f"  summary={result.summary}")
+        
+        # 验证检测结果
+        assert result.detected == test_case["expected_detected"], \
+            f"[{case_id}] 检测结果不符：expected {test_case['expected_detected']}, got {result.detected}"
+        
+        if test_case["expected_detected"]:
+            assert result.category == test_case["expected_category"], \
+                f"[{case_id}] 类别不符：expected {test_case['expected_category']}, got {result.category}"
+            
+            # 验证严重程度（使用顺序比较）
+            severity_order = ["none", "low", "medium", "high"]
+            min_idx = severity_order.index(test_case["expected_severity_min"])
+            result_idx = severity_order.index(result.severity)
+            assert result_idx >= min_idx, \
+                f"[{case_id}] 严重程度不足：expected >= {test_case['expected_severity_min']}, got {result.severity}"
+
+    def test_build_response_prefix_detected(self, api_key):
+        """测试：检测到问题时生成前缀"""
+        screener = PsychoScreener(api_key=api_key)
+        
+        result = ScreeningResult(
+            detected=True,
+            category="bullying",
+            severity="medium",
+            summary="孩子描述被同学欺负",
+            suggestion="建议与家长沟通",
+            raw_response="",
+        )
+        
+        prefix = screener.build_response_prefix(result)
+        print(f"\nprefix: {prefix}")
+        
+        assert "已发现特定心理问题" in prefix
+        assert "bullying" in prefix
+        assert "medium" in prefix
+
+    def test_build_response_prefix_not_detected(self, api_key):
+        """测试：未检测到问题时返回空前缀"""
+        screener = PsychoScreener(api_key=api_key)
+        
+        result = ScreeningResult(
+            detected=False,
+            category="none",
+            severity="none",
+            summary="未检测到心理问题",
+            suggestion="",
+            raw_response="",
+        )
+        
+        prefix = screener.build_response_prefix(result)
+        assert prefix == ""
+
+    def test_json_parse_error_handling(self, api_key, requests_mock):
+        """测试：模型返回非 JSON 时优雅处理"""
+        requests_mock.post(
+            "https://api.minimaxi.com/v1/text/chatcompletion_v2",
+            text="This is not JSON response",
+        )
+        
+        screener = PsychoScreener(api_key=api_key)
+        result = screener.screen("some context")
+        
+        # 应该优雅降级，不抛出异常
+        assert result.detected is False
+        assert "API调用失败" in result.summary
+
+
+# ============================================================================
+# 测试：数据结构验证
+# ============================================================================
+
+@pytest.mark.unit
+class TestScreeningResultModel:
+    """测试 ScreeningResult 数据模型"""
+
+    def test_screening_result_valid(self):
+        """测试有效数据"""
+        result = ScreeningResult(
+            detected=True,
+            category="bullying",
+            severity="high",
+            summary="test",
+            suggestion="test",
+            raw_response="test",
+        )
+        assert result.detected is True
+        assert result.category == "bullying"
+
+    def test_screening_result_defaults(self):
+        """测试默认值"""
+        result = ScreeningResult(detected=False)
+        assert result.detected is False
+        assert result.category == "none"
+        assert result.severity == "none"
+        assert result.summary == ""
+
+
+# ============================================================================
+# 测试：集成小智AI流程
+# ============================================================================
+
+@pytest.mark.integration
+class TestXiaozhiIntegration:
+    """
+    测试：与小智AI的集成流程
+    
+    假设 xiaozhi-esp32-server 返回的对话上下文经过本筛查器，
+    验证端到端的处理流程
+    """
+
+    def test_full_flow_with_bullying(self):
+        """
+        模拟完整流程：
+        1. 孩子说了一段可能被霸凌的话
+        2. 筛查器识别出问题
+        3. 原有回复被加上前缀
+        """
+        # 模拟 xiaozhi 返回的原始回复
+        original_response = "不要难过的小朋友，每个人都值得被友好对待哦。"
+        
+        # 这里需要真实 API key
+        api_key = os.environ.get("MINIMAX_API_KEY", "")
+        if not api_key:
+            pytest.skip("MINIMAX_API_KEY not set")
+        
+        screener = PsychoScreener(api_key=api_key)
+        
+        context = (
+            "孩子：今天小朋友又欺负我了，把我推倒在地上，还骂我。\n"
+            "孩子：我觉得好委屈，为什么他们要这样对我？"
+        )
+        
+        # 步骤1：筛查
+        screening_result = screener.screen(context)
+        print(f"\n[flow] screening result: {screening_result.model_dump_json(indent=2)}")
+        
+        # 步骤2：构建前缀
+        prefix = screener.build_response_prefix(screening_result)
+        
+        # 步骤3：覆盖原有回复
+        if prefix:
+            final_response = f"{prefix}\n\n{original_response}"
+        else:
+            final_response = original_response
+        
+        print(f"\n[flow] final response:\n{final_response}")
+        
+        # 验证前缀存在
+        if screening_result.detected:
+            assert "已发现特定心理问题" in final_response
+        else:
+            assert "已发现特定心理问题" not in final_response
+
+
+import os  # for integration tests