Skill: zhenstaff/talk-analyzer (version 1.0.0)

# Talk Analyzer - AI 对话分析技能 ## 技能概述 Talk Analyzer 是一个强大的 AI 驱动的商务对话分析工具，能够自动从会议记录、销售电话、客户支持对话中提取关键信息、行动项和策略建议。 ## 核心能力 ### 1. 多类型对话分析 - **会议分析** - 自动提取会议要点、决策和待办事项 - **销售分析** - 识别客户异议、购买信号和跟进策略 - **客服分析** - 评估服务质量、客户满意度和问题解决效率 - **谈判分析** - 分析双方立场、让步和谈判策略 - **面试分析** - 评估候选人资质和文化契合度 - **通用分析** - 适用于任何类型的对话内容 ### 2. 智能提取功能 - **对话摘要** - 生成 2-3 句话的精炼总结 - **关键要点** - 自动提取讨论的主要话题和结论 - **行动项提取** - 识别任务、负责人和截止日期 - **决策记录** - 记录重要决定及其理由 - **情感分析** - 评估对话情绪和参与度 - **发言者画像** - 分析每个参与者的贡献和风格 ### 3. 销售专用功能 - **异议识别** - 自动识别客户提出的顾虑（价格、时间、功能等） - **购买信号** - 发现客户的购买意向和紧迫性 - **机会点** - 识别痛点、预算信号和决策权 - **跟进建议** - 基于对话内容生成下一步行动策略 ### 4. AI 引擎支持 - **Claude (Anthropic)** - 主要分析引擎，提供深度理解 - **OpenAI GPT** - 备选引擎，提供多样化分析 - **本地 LLM** - 支持私密对话的本地处理 ## 使用场景 ### 场景 1: 团队会议总结 **输入**: 1小时的团队会议记录 **输出**: - 会议摘要 - 5-10 个关键决策点 - 每个人的待办事项（含截止日期） - 需要后续讨论的话题 **价值**: 节省 30 分钟的会议纪要整理时间 ### 场景 2: 销售电话分析 **输入**: 销售通话文字记录 **输出**: - 客户需求和痛点 - 提出的异议及严重程度 - 购买信号和时间线 - 跟进策略建议 **价值**: 提高销售转化率 20-30% ### 场景 3: 客服质量评估 **输入**: 客服对话记录 **输出**: - 客户满意度评分 - 问题解决质量 - 客服沟通技巧评估 - 改进建议 **价值**: 优化客服培训，提升客户满意度 ### 场景 4: 批量对话分析 **输入**: 多个对话文件 **输出**: - 每个对话的独立分析 - 跨对话的模式识别 - 趋势分析和洞察 **价值**: 发现系统性问题和机会 ## 技能参数 ### 必需参数 - `input` - 对话文本文件路径 - `type` - 分析类型（meeting/sales/support/negotiation/interview/general） ### 可选参数 - `sentiment` - 启用情感分析（布尔值） - `speakers` - 参与者姓名列表（逗号分隔） - `focus` - 关注的特定方面（如 "pricing,timeline,objections"） - `detailed` - 生成详细分析报告（布尔值） - `output` - 输出文件路径 - `format` - 输出格式（json/markdown） ## 使用示例 ### 基础用法 ```bash openclaw-talk analyze \ --input meeting.txt \ --type meeting ``` ### 高级用法 ```bash openclaw-talk analyze \ --input sales-call.txt \ --type sales \ --sentiment \ --speakers "John,Sarah" \ --focus "objections,pricing,timeline" \ --detailed \ --output report.json ``` ### 批量分析 ```bash openclaw-talk batch \ --config batch-config.json \ --output-dir ./reports/ ``` ### 对话对比 ```bash openclaw-talk compare \ --inputs "call1.txt,call2.txt,call3.txt" \ --type sales \ --output comparison.md ``` ## 输出示例 ### JSON 格式输出 ```json { "type": "meeting", "summary": "团队讨论了 Q2 移动应用发布计划，确定4月底上线核心功能，离线模式推迟到5月。", "key_points": [ "移动应用开发进度 70%", "推送通知需要 2 周，离线模式需要 4-5 周", "决定先发布核心功能，后续迭代添加离线模式", "批准营销预算 $30,000" ], "action_items": [ { "task": "设置 50-100 人的 beta 测试计划", "owner": "Bob", "deadline": "明天", "priority": "high" }, { "task": "制定营销策略并分享", "owner": "Charlie", "deadline": "周五", "priority": "high" } ], "decisions": [ { "decision": "同时发布 iOS 和 Android 版本", "made_by": "Alice", "rationale": "用户群体平均分布" } ], "sentiment": { "overall": "positive", "participants": { "Alice": {"sentiment": "positive", "engagement_level": "high"}, "Bob": {"sentiment": "neutral", "engagement_level": "medium"}, "Charlie": {"sentiment": "positive", "engagement_level": "high"} } } } ``` ## 技术要求 ### 环境要求 - Node.js 18.0.0 或更高版本 - npm 或 pnpm 包管理器 ### API 密钥 需要以下至少一个 AI 服务的 API 密钥： - Anthropic Claude API Key - OpenAI API Key - 或本地 LLM 服务端点 ### 输入格式 - 支持纯文本文件 (.txt) - 推荐格式: 每个发言者独立一行 - 建议最小长度: 50 字符 - 支持自动语言检测 ## 性能指标 - **分析速度**: 1000 字对话约 10-20 秒 - **准确率**: 关键信息提取准确率 > 90% - **支持语言**: 主要支持中文和英文 - **最大输入**: 单次分析最多 100,000 字符 ## 优势特点 1. **高度自动化** - 无需人工整理，一键生成结构化报告 2. **多场景适配** - 6 种预设分析类型，覆盖常见商务场景 3. **深度洞察** - 不仅提取信息，还提供策略建议 4. **灵活配置** - 丰富的参数支持定制化分析 5. **批量处理** - 支持同时分析多个对话文件 6. **隐私保护** - 支持本地 LLM，敏感对话不出本地 ## 典型工作流 1. **收集对话** - 从会议、电话录音转文字 2. **配置分析** - 选择分析类型和关注点 3. **执行分析** - 运行 AI 分析引擎 4. **查看结果** - 获取结构化报告 5. **采取行动** - 基于洞察执行后续任务 ## 集成能力 ### 命令行集成 ```bash # 在脚本中使用 ./analyze.sh analyze --input "$MEETING_FILE" --type meeting > report.json ``` ### 编程接口 ```typescript import { ConversationAnalyzer } from 'openclaw-talk-analyzer'; const analyzer = new ConversationAnalyzer({...}); const result = await analyzer.analyze(input, options); ``` ### 批处理 ```bash # 定时任务分析每日会议 crontab -e 0 18 * * * /path/to/openclaw-talk batch --config daily-config.json ``` ## 更新计划 ### v1.1.0 (即将推出) - 实时音频转录集成 - PDF 报告导出 - Web 可视化仪表板 ### v1.2.0 (规划中) - 多语言支持扩展 - CRM 系统集成（Salesforce, HubSpot） - 团队协作功能 ## 支持和文档 - **完整文档**: https://github.com/ZhenRobotics/openclaw-talk-analyzer - **快速开始**: 见项目 QUICKSTART.md - **示例文件**: 包含真实场景示例 - **问题反馈**: GitHub Issues ## 许可证 MIT License - 开源免费使用 --- **让 AI 帮你从对话中提取价值，把时间用在更重要的事情上！**

path: readme.md
content: # Talk Analyzer

**AI-Powered Business Conversation Analysis**

Transform raw conversation data into actionable business intelligence. Automatically extract summaries, action items, decisions, and strategic insights from meetings, sales calls, and customer interactions.

---

## 🎯 What It Does

Talk Analyzer uses AI (Claude, GPT) to analyze business conversations and generate:

- **Meeting Summaries** - Key points, decisions, and action items
- **Sales Insights** - Objections, opportunities, and next steps
- **Support Quality** - Sentiment analysis and resolution assessment
- **Strategic Recommendations** - Data-driven follow-up suggestions
- **Speaker Analysis** - Individual participation and engagement metrics

---

## ✨ Key Features

### Multiple Analysis Types
- 📊 **Meeting** - Extract decisions and action items
- 💰 **Sales** - Identify objections and buying signals
- 🎧 **Support** - Evaluate service quality and satisfaction
- 🤝 **Negotiation** - Analyze positions and strategies
- 👥 **Interview** - Assess qualifications and fit
- 📝 **General** - Analyze any conversation

### Intelligent Extraction
- ✅ Automatic action item detection with owners and deadlines
- 💭 Sentiment analysis per participant
- 🎯 Topic detection and frequency tracking
- 📈 Trend analysis across multiple conversations
- 🔍 Custom focus areas (pricing, timeline, technical details, etc.)

### Flexible Output
- 📄 JSON format for programmatic use
- 📝 Markdown format for human reading
- 🔄 Batch processing for multiple files
- 📊 Comparison reports across conversations

---

## 🚀 Quick Start

### Installation

```bash
# Clone from GitHub
git clone https://github.com/ZhenRobotics/openclaw-talk-analyzer.git
cd openclaw-talk-analyzer
npm install

# Or install from npm (after publication)
npm install -g openclaw-talk-analyzer
```

### Configuration

```bash
# Copy environment template
cp .env.example .env

# Add your AI API key (choose one)
ANTHROPIC_API_KEY=your-claude-api-key
# OR
OPENAI_API_KEY=your-openai-api-key
```

Get API keys:
- **Claude**: https://console.anthropic.com/
- **OpenAI**: https://platform.openai.com/api-keys

### First Analysis

```bash
# Analyze a meeting
openclaw-talk analyze \
  --input meeting.txt \
  --type meeting \
  --sentiment

# Analyze a sales call
openclaw-talk analyze \
  --input sales-call.txt \
  --type sales \
  --focus "objections,pricing,timeline" \
  --output report.json
```

---

## 💻 Usage Examples

### Basic Analysis

```bash
openclaw-talk analyze \
  --input conversation.txt \
  --type meeting
```

### Detailed Analysis with Speakers

```bash
openclaw-talk analyze \
  --input team-discussion.txt \
  --type meeting \
  --speakers "Alice,Bob,Charlie" \
  --sentiment \
  --detailed \
  --output detailed-report.json
```

### Batch Processing

Create `batch-config.json`:
```json
{
  "files": [
    {
      "input": "meeting-2024-03-01.txt",
      "type": "meeting",
      "speakers": ["Alice", "Bob"]
    },
    {
      "input": "sales-call-acme.txt",
      "type": "sales",
      "focus": ["objections", "pricing"]
    }
  ],
  "options": {
    "sentiment": true,
    "detailed": true
  }
}
```

Run batch analysis:
```bash
openclaw-talk batch --config batch-config.json --output-dir ./reports/
```

### Compare Conversations

```bash
openclaw-talk compare \
  --inputs "call1.txt,call2.txt,call3.txt" \
  --type sales \
  --output comparison-report.md
```

---

## 📊 Output Example

### Meeting Analysis Result

```json
{
  "type": "meeting",
  "summary": "Team discussed Q2 product roadmap and resource allocation. Decided to prioritize mobile app launch by end of April.",
  "key_points": [
    "Mobile app 70% complete",
    "Need 2 weeks for push notifications",
    "Beta testing with 50-100 users planned",
    "Marketing budget approved: $30,000"
  ],
  "action_items": [
    {
      "task": "Set up beta program",
      "owner": "Bob",
      "deadline": "Tomorrow",
      "priority": "high"
    },
    {
      "task": "Draft marketing strategy",
      "owner": "Charlie",
      "deadline": "Friday",
      "priority": "high"
    }
  ],
  "decisions": [
    {
      "decision": "Launch iOS and Android simultaneously",
      "made_by": "Alice",
      "rationale": "User base is evenly split"
    }
  ],
  "sentiment": {
    "overall": "positive",
    "participants": {
      "Alice": {"sentiment": "positive", "engagement_level": "high"},
      "Bob": {"sentiment": "neutral", "engagement_level": "medium"},
      "Charlie": {"sentiment": "positive", "engagement_level": "high"}
    }
  }
}
```

---

## 🎯 Use Cases

### 1. Sales Teams
- Analyze calls to identify objection patterns
- Track buying signals and urgency indicators
- Generate data-driven follow-up strategies
- Compare successful vs unsuccessful calls

### 2. Product Managers
- Extract feature requests from user interviews
- Identify common pain points across conversations
- Prioritize roadmap based on customer feedback
- Track sentiment trends over time

### 3. Customer Success
- Monitor support conversation quality
- Evaluate agent performance objectively
- Identify at-risk customers early
- Improve training based on conversation analysis

### 4. Executives
- Get meeting summaries without attending
- Track team decision patterns
- Identify strategic opportunities from customer conversations
- Monitor team engagement and morale

---

## 🛠️ Available Commands

```bash
# Analyze single conversation
openclaw-talk analyze [options]

# Batch process multiple files
openclaw-talk batch --config <file>

# Compare multiple conversations
openclaw-talk compare --inputs <files>

# List available AI engines
openclaw-talk engines

# View analysis history
openclaw-talk history
```

**Command Aliases:**
- `openclaw-talk` - Primary command
- `talk-analyzer` - Alternative command
- `talkz` - Quick shorthand

---

## 🔧 Configuration Options

### Analysis Options
- `--type` - Analysis type (meeting, sales, support, negotiation, interview, general)
- `--input` - Input conversation file path
- `--output` - Output file path (optional, defaults to stdout)
- `--format` - Output format (json, markdown)
- `--sentiment` - Enable sentiment analysis
- `--speakers` - Comma-separated list of speaker names
- `--focus` - Specific aspects to focus on (comma-separated)
- `--detailed` - Include detailed analysis and quotes
- `--model` - AI model to use (claude-sonnet-4, gpt-4, etc.)

---

## 📚 Documentation

- **Full README**: [GitHub Repository](https://github.com/ZhenRobotics/openclaw-talk-analyzer)
- **Quick Start Guide**: See QUICKSTART.md in repository
- **Example Files**: Sample conversations included in `examples/` directory
- **API Documentation**: TypeScript definitions in `src/core/types.ts`

---

## 🔐 Privacy & Security

- **No Data Storage**: Conversations are not stored on external servers (except when using cloud AI APIs)
- **Local Processing**: Support for local LLM models for sensitive conversations
- **API Key Security**: Keys stored locally in `.env` file, never committed to version control
- **GDPR Compliant**: Designed with privacy regulations in mind

---

## 🌟 Why Choose Talk Analyzer?

### Compared to Manual Summarization
- ⚡ **10x Faster** - Analyze in seconds vs. 30+ minutes manual work
- 🎯 **More Consistent** - No human bias or fatigue
- 📊 **Structured Output** - Ready for database/CRM integration
- 🔄 **Scalable** - Process hundreds of conversations easily

### Compared to Generic AI Tools
- 🎯 **Purpose-Built** - Optimized for business conversations
- 📋 **Structured Results** - JSON output with typed fields
- 🔧 **Customizable** - Focus on specific aspects relevant to your business
- 🚀 **CLI & API** - Easily integrate into workflows

---

## 🛣️ Roadmap

### v1.1.0 (Coming Soon)
- Real-time audio transcription integration
- PDF report generation
- Web dashboard for visualization
- Local LLM support (Ollama)

### v1.2.0 (Planned)
- Multi-language support
- CRM integrations (Salesforce, HubSpot)
- Slack/Teams bot integration
- Advanced analytics dashboard

### v2.0.0 (Future)
- Team collaboration features
- Custom analysis templates
- API webhooks for automation
- Enterprise features (SSO, RBAC)

---

## 🤝 Support

- **GitHub Issues**: [Report bugs or request features](https://github.com/ZhenRobotics/openclaw-talk-analyzer/issues)
- **Documentation**: [Full docs and guides](https://github.com/ZhenRobotics/openclaw-talk-analyzer#readme)
- **Examples**: Check the `examples/` directory for sample conversations

---

## 📄 License

MIT License - Free for personal and commercial use

---

## 🚀 Getting Started Checklist

- [ ] Install Node.js 18+ and npm
- [ ] Clone repository or install from npm
- [ ] Get API key from Claude or OpenAI
- [ ] Configure `.env` file with API key
- [ ] Try analyzing example conversation files
- [ ] Analyze your own conversation transcripts
- [ ] Explore batch processing for multiple files
- [ ] Integrate into your workflow

---

## 📝 Example Conversation Format

For best results, format your conversation files like this:

```
Alice: Hi everyone, thanks for joining today's meeting.

Bob: Happy to be here. Let's discuss the project timeline.

Charlie: I think we can launch by end of Q2 if we focus on core features.

Alice: Agreed. Bob, can you lead the technical planning?

Bob: Sure, I'll have a proposal ready by Friday.
```

---

## 💡 Tips for Best Results

1. **Clear Speaker Attribution** - Use consistent speaker names
2. **Minimum Length** - At least 50 characters for meaningful analysis
3. **Context Matters** - Provide background in metadata or focus parameters
4. **Specific Focus** - Use `--focus` to highlight important aspects
5. **Review Output** - AI analysis is highly accurate but review for critical decisions

---

**Transform your conversations into actionable intelligence with AI!**

For more information, visit: https://github.com/ZhenRobotics/openclaw-talk-analyzer


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)