OCTO-ONA

Organizational Network Analysis Framework for Human-AI Collaboration

Version 3.0 — Conversational ONA with Natural Language Interface 🎯

English | 中文

🎉 What's New in v3.0

💬 Conversational ONA (Layer 7)

用自然语言查询和分析组织网络数据！

🗣️ Natural Language Queries — "我的 Hub Score 是多少？"、"本周的活跃度如何？"
🔐 Fine-grained Permissions — 只能查询自己和自己拥有的 Bot
🤖 Intelligent Responses — 自动生成友好的摘要和建议
💬 Multi-turn Conversations — 代词消解、时间范围继承
📊 5 Intent Types — Metrics, Network, Ranking, Trends, Reports
🚀 User Onboarding — 欢迎消息、帮助指南、场景化入口
😊 Friendly Errors — 5 类错误的智能提示和建议

Try it now:

我的 Hub Score 是多少？
本周的活跃度如何？
Top 5 最活跃的人是谁？
给我一份本周报告

📖 User Guide | 🔧 API Reference | 📋 Changelog

🗺️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                      Layer 7: Conversational ONA (v3.0)     │
│   Natural Language Query → Intent → Permission → Response  │
└─────────────────────────────────────────────────────────────┘
                              ↓
┌─────────────────────────────────────────────────────────────┐
│                      Layers 1-6: Core ONA (v2.0)            │
│   Metrics Engine → Network Analysis → Dashboard            │
└─────────────────────────────────────────────────────────────┘

🎉 What's New in v2.0

🗄️ Database-Driven Architecture — All metric definitions stored in MySQL (no hardcoded logic)
💾 Local Data Caching — Sync remote data once, analyze offline forever
🚀 CLI Tools — octo-ona sync for data synchronization
🔧 Dynamic Metrics — Add/modify metrics via database (no code changes)
📈 Result Caching — Store analysis results with version tracking
🧪 E2E Tests — Full workflow validation (54 tests passing)

📋 View Changelog | 🚀 Quick Start | 📖 Migration Guide

English

Overview

OCTO-ONA is a TypeScript/Node.js framework for analyzing organizational networks with a focus on Human-AI collaboration and "connoisseurship" (品鉴) metrics.

Key Features:

🤖 Human-AI Collaboration Analysis — Bot functional tags (T1-T5)
🏆 Connoisseurship Metrics — Hub Score, influence breadth, execution conversion
📊 15 Core Metrics — Network, collaboration, and connoisseurship indicators
🎨 Interactive Dashboard — Single-page HTML with ECharts visualizations
🔌 Pluggable Data Sources — Base adapter for any data source (DMWork included)

Quick Start

🚀 Fastest Way to Get Started

Just want to see the UI?

git clone https://github.com/callme-YZ/OCTO-ONA.git
cd OCTO-ONA
npm install
npm run start:ui

Then open http://localhost:3000 in your browser!

Click 🇬🇧 EN or 🇨🇳 中文 to switch language
Follow the 4-step wizard to analyze your data

Installation

# Clone repository
git clone https://github.com/callme-YZ/OCTO-ONA.git
cd OCTO-ONA

# Install dependencies
npm install

# Build
npm run build

Run Demo

# Generate demo dashboard
npx ts-node examples/dashboard-demo.ts

# View dashboard
open demo-dashboard.html

End-to-End Demo

# Complete pipeline: Data → Analysis → Metrics → Dashboard
npx ts-node examples/end-to-end-demo.ts

# View output
open e2e-dashboard.html

v1.1 Features (New!)

Network preview before analysis

🌐 Web Configuration UI

Visual interface for non-technical users:

# Start Web UI (one command!)
npm run start:ui

# Or use development mode (auto-reload)
npm run dev:ui

# Then open in browser
# http://localhost:3000

Features:

4-step wizard (Data Source → Filters → Preview → Run)
Real-time connection testing
Network preview before analysis
Multi-language support (🇬🇧 EN / 🇨🇳 中文)
Supports Discord & GitHub (DMWork via CLI)
Supports Discord & GitHub (DMWork via CLI)

🔌 New Adapters

Discord Adapter:

import { DiscordAdapter } from './src/layer1/adapters/discord-adapter';

const adapter = new DiscordAdapter();
await adapter.connect({ token: 'YOUR_BOT_TOKEN', guildId: 'YOUR_GUILD_ID' });

const graph = await adapter.extractNetwork({
  channelIds: ['channel1', 'channel2'],
  startTime: new Date('2026-01-01'),
  endTime: new Date('2026-01-31'),
});

GitHub Adapter:

import { GitHubAdapter } from './src/layer1/adapters/github-adapter';

const adapter = new GitHubAdapter();
await adapter.connect({ token: 'ghp_xxxxx', owner: 'facebook', repo: 'react' });

const graph = await adapter.extractNetwork({
  issueStates: ['open', 'closed'],
  startTime: new Date('2026-01-01'),
  endTime: new Date('2026-01-31'),
});

📊 Export Formats

PDF Export:

import { PDFExporter } from './src/layer6/pdf-exporter';

const exporter = new PDFExporter();
const pdfBuffer = await exporter.generate(graph, metrics, {
  title: 'ONA Report',
  author: 'Your Name',
});

fs.writeFileSync('report.pdf', pdfBuffer);

Excel Export:

import { ExcelExporter } from './src/layer6/excel-exporter';

const exporter = new ExcelExporter();
const excelBuffer = await exporter.export(metrics);

fs.writeFileSync('metrics.xlsx', excelBuffer);

PNG Charts:

import { ImageExporter } from './src/layer6/image-exporter';

const exporter = new ImageExporter();

// Network graph
const networkImg = await exporter.exportNetworkGraph(graph);
fs.writeFileSync('network.png', networkImg);

// Hub score chart
const chartImg = await exporter.exportHubScoreChart(metrics);
fs.writeFileSync('hubscore.png', chartImg);

REST API:

import { APIServer } from './src/layer6/api-server';

const server = new APIServer({ port: 3000, cors: true });

server.store('analysis-1', graph, metrics);

await server.start(3000);
// GET /api/v1/graphs
// GET /api/v1/graph/:id
// GET /api/v1/metrics/:id

📈 Advanced Analytics

Comparison Analysis:

import { ComparisonAnalyzer } from './src/layer5/comparison-analyzer';

const analyzer = new ComparisonAnalyzer();

const report = analyzer.compare(
  [graphWeek1, graphWeek2],
  [metricsWeek1, metricsWeek2],
  { dimension: 'time', labels: ['Week 1', 'Week 2'] }
);

console.log(`Hub Score change: ${report.changes[0].changeRate}%`);
console.log(`New nodes: ${report.nodeChanges.added.length}`);

Trend Analysis:

import { TrendAnalyzer } from './src/layer5/trend-analyzer';

const analyzer = new TrendAnalyzer();

const report = analyzer.analyzeTrend(
  [graph1, graph2, graph3],
  [metrics1, metrics2, metrics3],
  { predictNext: true }
);

console.log(`Predicted density: ${report.trends[0].prediction}`);
console.log(`Anomalies detected: ${report.trends[0].anomalies.length}`);

Health Monitoring:

import { HealthMonitor } from './src/layer5/health-monitor';

const monitor = new HealthMonitor();

const report = monitor.detectAnomalies(graph, metrics);

console.log(`Health score: ${report.score}/100`);
console.log(`Risk level: ${report.riskLevel}`);

for (const issue of report.issues) {
  console.log(`- ${issue.type}: ${issue.description}`);
  console.log(`  Recommendation: ${issue.recommendation}`);
}

Smart Recommendations:

import { Recommender } from './src/layer5/recommender';

const recommender = new Recommender();

const recommendations = recommender.suggestMentions('user-123', graph, metrics, { limit: 5 });

for (const rec of recommendations) {
  console.log(`@${rec.targetName}: ${rec.reason} (score: ${rec.score})`);
}

v1.1 Features (New!)

🌐 Web Configuration UI

Visual interface for non-technical users:

# Start web UI server
npx ts-node -e "import { ConfigServer } from './src/web-ui/config-server'; const server = new ConfigServer(); server.start(3000);"

# Open browser
open http://localhost:3000

Features:

4-step wizard (Data Source → Filters → Preview → Run)
Real-time connection testing
Network preview before analysis
Supports Discord & GitHub (DMWork via CLI)

🔌 New Adapters

Discord Adapter:

import { DiscordAdapter } from './src/layer1/adapters/discord-adapter';

const adapter = new DiscordAdapter();
await adapter.connect({ token: 'YOUR_BOT_TOKEN', guildId: 'YOUR_GUILD_ID' });

const graph = await adapter.extractNetwork({
  channelIds: ['channel1', 'channel2'],
  startTime: new Date('2026-01-01'),
  endTime: new Date('2026-01-31'),
});

GitHub Adapter:

import { GitHubAdapter } from './src/layer1/adapters/github-adapter';

const adapter = new GitHubAdapter();
await adapter.connect({ token: 'ghp_xxxxx', owner: 'facebook', repo: 'react' });

const graph = await adapter.extractNetwork({
  issueStates: ['open', 'closed'],
  startTime: new Date('2026-01-01'),
  endTime: new Date('2026-01-31'),
});

📊 Export Formats

PDF Export:

import { PDFExporter } from './src/layer6/pdf-exporter';

const exporter = new PDFExporter();
const pdfBuffer = await exporter.generate(graph, metrics, {
  title: 'ONA Report',
  author: 'Your Name',
});

fs.writeFileSync('report.pdf', pdfBuffer);

Excel Export:

import { ExcelExporter } from './src/layer6/excel-exporter';

const exporter = new ExcelExporter();
const excelBuffer = await exporter.export(metrics);

fs.writeFileSync('metrics.xlsx', excelBuffer);

PNG Charts:

import { ImageExporter } from './src/layer6/image-exporter';

const exporter = new ImageExporter();

// Network graph
const networkImg = await exporter.exportNetworkGraph(graph);
fs.writeFileSync('network.png', networkImg);

// Hub score chart
const chartImg = await exporter.exportHubScoreChart(metrics);
fs.writeFileSync('hubscore.png', chartImg);

REST API:

import { APIServer } from './src/layer6/api-server';

const server = new APIServer({ port: 3000, cors: true });

server.store('analysis-1', graph, metrics);

await server.start(3000);
// GET /api/v1/graphs
// GET /api/v1/graph/:id
// GET /api/v1/metrics/:id

📈 Advanced Analytics

Comparison Analysis:

import { ComparisonAnalyzer } from './src/layer5/comparison-analyzer';

const analyzer = new ComparisonAnalyzer();

const report = analyzer.compare(
  [graphWeek1, graphWeek2],
  [metricsWeek1, metricsWeek2],
  { dimension: 'time', labels: ['Week 1', 'Week 2'] }
);

console.log(`Hub Score change: ${report.changes[0].changeRate}%`);
console.log(`New nodes: ${report.nodeChanges.added.length}`);

Trend Analysis:

import { TrendAnalyzer } from './src/layer5/trend-analyzer';

const analyzer = new TrendAnalyzer();

const report = analyzer.analyzeTrend(
  [graph1, graph2, graph3],
  [metrics1, metrics2, metrics3],
  { predictNext: true }
);

console.log(`Predicted density: ${report.trends[0].prediction}`);
console.log(`Anomalies detected: ${report.trends[0].anomalies.length}`);

Health Monitoring:

import { HealthMonitor } from './src/layer5/health-monitor';

const monitor = new HealthMonitor();

const report = monitor.detectAnomalies(graph, metrics);

console.log(`Health score: ${report.score}/100`);
console.log(`Risk level: ${report.riskLevel}`);

for (const issue of report.issues) {
  console.log(`- ${issue.type}: ${issue.description}`);
  console.log(`  Recommendation: ${issue.recommendation}`);
}

Smart Recommendations:

import { Recommender } from './src/layer5/recommender';

const recommender = new Recommender();

const recommendations = recommender.suggestMentions('user-123', graph, metrics, { limit: 5 });

for (const rec of recommendations) {
  console.log(`@${rec.targetName}: ${rec.reason} (score: ${rec.score})`);
}

Core Concepts

1. Data Model (Layer 2)

NetworkGraph consists of:

human_nodes — Human participants
ai_agent_nodes — AI bots/agents
edges — Connections with weights
messages — Communication records

Supported Edge Types:

H2H — Human to Human
H2B — Human to Bot
B2H — Bot to Human
B2B — Bot to Bot

2. Metrics (Layer 4)

15 Core Metrics (13 P0 + 2 P1):

Network Metrics (L1):

L1.1 Degree Centrality
L1.2 Betweenness Centrality
L1.3 Closeness Centrality (P1)
L1.4 Network Density
L1.5 Leadership Distance
L1.6 Silo Index
L1.7 Burnout Risk

Collaboration Metrics (L2):

L2.1 Bot Functional Tags
L2.2 Human-Bot Ratio

Connoisseurship Metrics (L3):

L3.1 Connoisseurship Frequency
L3.2 Influence Breadth
L3.3 Execution Conversion
L3.4 Network Amplification (P1)
L3.5 Hub Score

3. Bot Functional Tags (T1-T5)

Multi-label system for bot characterization:

T1: Cross-Team Connector — Connects ≥3 teams
T2: Intra-Team Hub — High degree, low cross-team ratio
T3: Human Proxy — Strong edge weight to primary human
T4: Information Aggregator — High in/out message ratio
T5: High Activity — Message count ≥ P75

4. Hub Score

Definition: Mentions received / Messages sent

Layers:

L5 (HS > 3.0) — Strategic Authority
L4 (HS = ∞) — Pure Recipient
L3 (0.3 ≤ HS ≤ 3.0) — Bot Interface
L2 (0 < HS < 0.3) — Active Management
L1 (HS = 0) — Pure Execution
L0 — No Activity

Architecture

OCTO-ONA/
├── src/
│   ├── layer1/          # Data Adapters
│   │   ├── base-adapter.ts
│   │   ├── dmwork-adapter.ts
│   │   └── validator.ts
│   ├── layer2/          # Data Models
│   │   └── models.ts
│   ├── layer3/          # Analysis Engine
│   │   ├── analysis-engine.ts
│   │   └── connoisseur-detector.ts
│   ├── layer4/          # Metrics Calculator
│   │   ├── metrics-calculator.ts
│   │   ├── core-metrics.ts
│   │   └── bot-tagger.ts
│   └── layer6/          # Visualization
│       ├── dashboard-generator.ts
│       └── dashboard-template.html
├── tests/               # Unit Tests (88 tests)
├── examples/            # Demo Scripts
├── docs/                # Documentation
└── README.md

Usage

Basic Usage

import { DashboardGenerator } from 'octo-ona';
import { NetworkGraph } from 'octo-ona';

// 1. Prepare your network data
const graph: NetworkGraph = {
  graph_id: 'my_network',
  human_nodes: [...],
  ai_agent_nodes: [...],
  edges: [...],
  messages: [...],
  // ... other fields
};

// 2. Generate dashboard
const generator = new DashboardGenerator(graph);
await generator.generate('./output.html');

Calculate Specific Metrics

import { MetricsCalculator, ALL_CORE_METRICS } from 'octo-ona';

const calculator = new MetricsCalculator(graph);
calculator.registerMetrics(ALL_CORE_METRICS);

// Calculate all P0 metrics
const results = await calculator.calculateAll('P0');

// Calculate specific metric
const hubScore = await calculator.calculateMetric('L3.5');

Custom Data Adapter

import { BaseAdapter } from 'octo-ona';

class MyAdapter extends BaseAdapter {
  async connect(config: any): Promise<void> {
    // Your connection logic
  }
  
  async extractNetwork(options: any): Promise<NetworkGraph> {
    // Your extraction logic
  }
  
  async disconnect(): Promise<void> {
    // Your cleanup logic
  }
}

API Reference

See API.md for detailed API documentation.

Testing

# Run all tests
npm test

# Run specific test suite
npm test tests/layer4.metrics-calculator.test.ts

# Coverage
npm test -- --coverage

Test Coverage: 88/88 passed (100%)

Performance

Benchmarks (MacBook Pro M1):

Network Size	Nodes	Messages	Time	Memory
Small	8	3,800	<1s	<100MB
Medium	50	20,000	~3s	<500MB
Large	200	100,000	~15s	<2GB

Known Limitations

graphology library constraints:
- Minimum 2 nodes required for centrality metrics
- Empty graphs not supported
Beta version limitations:
- L3.3 only detects bot responses (no GitHub Issue/PR)
- L3.4 uses name-based detection (no NLP similarity)

Contributing

Contributions welcome! Please:

Fork the repository
Create feature branch
Add tests for new features
Submit pull request

License

MIT License - see LICENSE for details.

Authors

Mayo — Product Design & Implementation
∞ — Coordination & Verification
YZ — Project Lead

Acknowledgments

Built with:

graphology — Graph analysis
ECharts — Visualizations
Zod — Schema validation
TypeScript — Type safety

中文

概述

OCTO-ONA 是一个用于分析人机协作组织网络的 TypeScript/Node.js 框架，专注于"品鉴"指标。

核心特性：

🤖 人机协作分析 — Bot功能标签（T1-T5）
🏆 品鉴指标 — Hub Score、影响广度、执行转化
📊 15个核心指标 — 网络、协作、品鉴三层指标
🎨 交互式仪表盘 — 单页HTML + ECharts可视化
🔌 可插拔数据源 — 基础适配器支持任意数据源（包含DMWork）

快速开始

安装

# 克隆仓库
git clone https://github.com/callme-YZ/OCTO-ONA.git
cd OCTO-ONA

# 安装依赖
npm install

# 构建
npm run build

运行Demo

# 生成Demo仪表盘
npx ts-node examples/dashboard-demo.ts

# 查看仪表盘
open demo-dashboard.html

端到端Demo

# 完整流程：数据 → 分析 → 指标 → 仪表盘
npx ts-node examples/end-to-end-demo.ts

# 查看输出
open e2e-dashboard.html

核心概念

1. 数据模型（Layer 2）

NetworkGraph 包含：

human_nodes — 人类参与者
ai_agent_nodes — AI机器人/代理
edges — 带权重的连接
messages — 通信记录

支持的边类型：

H2H — 人到人
H2B — 人到Bot
B2H — Bot到人
B2B — Bot到Bot

2. 指标（Layer 4）

15个核心指标（13个P0 + 2个P1）：

网络指标（L1）：

L1.1 度中心性
L1.2 中介中心性
L1.3 接近中心性（P1）
L1.4 网络密度
L1.5 领导层距离
L1.6 孤岛指数
L1.7 过载风险

协作指标（L2）：

L2.1 Bot功能标签
L2.2 人机协作比例

品鉴指标（L3）：

L3.1 品鉴行为频率
L3.2 品鉴影响广度
L3.3 品鉴执行转化
L3.4 品鉴网络放大（P1）
L3.5 Hub Score

3. Bot功能标签（T1-T5）

多标签Bot特征系统：

T1：跨团队连接 — 连接≥3个团队
T2：团队内枢纽 — 高度数，低跨团队比例
T3：人类代理 — 与主要人类强边权重
T4：信息聚合 — 高接收/发送消息比
T5：高活跃 — 消息数≥P75

4. Hub Score（中枢分数）

定义： 被@次数 / 发送消息数

层级：

L5 (HS > 3.0) — 战略权威
L4 (HS = ∞) — 纯接收者
L3 (0.3 ≤ HS ≤ 3.0) — Bot接口
L2 (0 < HS < 0.3) — 活跃管理
L1 (HS = 0) — 纯执行
L0 — 无活动

架构

OCTO-ONA/
├── src/
│   ├── layer1/          # 数据适配器
│   │   ├── base-adapter.ts
│   │   ├── dmwork-adapter.ts
│   │   └── validator.ts
│   ├── layer2/          # 数据模型
│   │   └── models.ts
│   ├── layer3/          # 分析引擎
│   │   ├── analysis-engine.ts
│   │   └── connoisseur-detector.ts
│   ├── layer4/          # 指标计算器
│   │   ├── metrics-calculator.ts
│   │   ├── core-metrics.ts
│   │   └── bot-tagger.ts
│   └── layer6/          # 可视化
│       ├── dashboard-generator.ts
│       └── dashboard-template.html
├── tests/               # 单元测试（88个测试）
├── examples/            # 示例脚本
├── docs/                # 文档
└── README.md

使用方法

基本使用

import { DashboardGenerator } from 'octo-ona';
import { NetworkGraph } from 'octo-ona';

// 1. 准备网络数据
const graph: NetworkGraph = {
  graph_id: 'my_network',
  human_nodes: [...],
  ai_agent_nodes: [...],
  edges: [...],
  messages: [...],
  // ... 其他字段
};

// 2. 生成仪表盘
const generator = new DashboardGenerator(graph);
await generator.generate('./output.html');

计算特定指标

import { MetricsCalculator, ALL_CORE_METRICS } from 'octo-ona';

const calculator = new MetricsCalculator(graph);
calculator.registerMetrics(ALL_CORE_METRICS);

// 计算所有P0指标
const results = await calculator.calculateAll('P0');

// 计算特定指标
const hubScore = await calculator.calculateMetric('L3.5');

自定义数据适配器

import { BaseAdapter } from 'octo-ona';

class MyAdapter extends BaseAdapter {
  async connect(config: any): Promise<void> {
    // 连接逻辑
  }
  
  async extractNetwork(options: any): Promise<NetworkGraph> {
    // 数据提取逻辑
  }
  
  async disconnect(): Promise<void> {
    // 清理逻辑
  }
}

API参考

详见 API.md

测试

# 运行所有测试
npm test

# 运行特定测试套件
npm test tests/layer4.metrics-calculator.test.ts

# 测试覆盖率
npm test -- --coverage

测试覆盖： 88/88 通过（100%）

性能

基准测试（MacBook Pro M1）：

网络规模	节点数	消息数	时间	内存
小型	8	3,800	<1秒	<100MB
中型	50	20,000	~3秒	<500MB
大型	200	100,000	~15秒	<2GB

已知限制

graphology库约束：
- 中心性指标至少需要2个节点
- 不支持空图
Beta版本限制：
- L3.3仅检测Bot响应（无GitHub Issue/PR）
- L3.4使用基于姓名的检测（无NLP相似度）

贡献

欢迎贡献！请：

Fork仓库
创建功能分支
为新功能添加测试
提交Pull Request

许可证

MIT许可证 - 详见 LICENSE

作者

Mayo — 产品设计与实现
∞ — 协调与验收
YZ — 项目负责人

致谢

构建工具：

graphology — 图分析
ECharts — 可视化
Zod — 模式验证
TypeScript — 类型安全

Version: 0.8.0
Last Updated: 2026-03-20

v1.2 Features - External Data Loading

OCTO-ONA now supports two dashboard generation modes:

1. Inline Data Mode (Original)

Single HTML file with all data embedded:

const generator = new DashboardGenerator(graph);
await generator.generate('./dashboard.html');

2. External Data Mode (New!)

Separate HTML and JSON files:

const generator = new DashboardGenerator(graph);
await generator.generateWithExternalData('./dashboard-dir');
// Creates:
//   dashboard-dir/index.html  (16KB - presentation only)
//   dashboard-dir/data.json   (4KB - all metrics data)

Benefits of external data mode:

✅ Separation of concerns - HTML for presentation, JSON for data
✅ Dynamic updates - Update data.json without regenerating HTML
✅ Version control friendly - Easier to diff data changes
✅ Smaller file sizes - HTML is ~16KB vs ~200KB+ inline mode
✅ RESTful architecture - Can serve data.json via API

Example:

# Generate demo with external data
npx ts-node examples/dashboard-demo-external.ts

# View in browser
open demo-dashboard-external/index.html

The HTML uses fetch('./data.json') to load data at runtime, making it perfect for:

CI/CD pipelines (update data without redeploying HTML)
Multi-environment dashboards (same HTML, different data.json)
Progressive web apps (cache HTML, fetch latest data)

CLI Usage (v2.0)

Sync Command

Sync data from remote database to local cache:

# Incremental sync (default)
npx octo-ona sync dmwork-octo

# Full sync
npx octo-ona sync dmwork-octo --full

# Specify time range
npx octo-ona sync dmwork-octo --start-time 2026-03-01 --end-time 2026-03-20

# Verbose output
npx octo-ona sync dmwork-octo --verbose

Configuration

Create config files in project root:

octo-remote.config.json (Remote database):

{
  "host": "remote.example.com",
  "port": 13306,
  "user": "readonly",
  "password": "your-password",
  "database": "im"
}

octo-ona.config.json (Local database):

{
  "host": "localhost",
  "port": 3306,
  "user": "root",
  "password": "",
  "database": "octo_ona"
}

Alternative: Use environment variables:

export OCTO_REMOTE_HOST=remote.example.com
export OCTO_REMOTE_PORT=13306
export OCTO_REMOTE_USER=readonly
export OCTO_REMOTE_PASSWORD=your-password
export OCTO_REMOTE_DATABASE=im

export OCTO_LOCAL_HOST=localhost
export OCTO_LOCAL_PORT=3306
export OCTO_LOCAL_USER=root
export OCTO_LOCAL_PASSWORD=
export OCTO_LOCAL_DATABASE=octo_ona

Name		Name	Last commit message	Last commit date
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data		data
docs		docs
examples		examples
migrations		migrations
scripts		scripts
src		src
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CONNOISSEURSHIP_IMPLEMENTATION_REPORT.md		CONNOISSEURSHIP_IMPLEMENTATION_REPORT.md
EXECUTION_REPORT.md		EXECUTION_REPORT.md
GIT_COMMIT_READY.md		GIT_COMMIT_READY.md
GIT_READY_REPORT.md		GIT_READY_REPORT.md
INTERACTIVE_FEATURES_GUIDE.md		INTERACTIVE_FEATURES_GUIDE.md
README.md		README.md
ROADMAP.md		ROADMAP.md
SCHEMA-V2.md		SCHEMA-V2.md
TASK_COMPLETION_SUMMARY.md		TASK_COMPLETION_SUMMARY.md
TASK_SUMMARY.md		TASK_SUMMARY.md
jest.config.js		jest.config.js
package-lock.json		package-lock.json
package.json		package.json
schema-v2.sql		schema-v2.sql
seed-metrics.sql		seed-metrics.sql
test-migration.sh		test-migration.sh
tsconfig.json		tsconfig.json

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OCTO-ONA

🎉 What's New in v3.0

💬 Conversational ONA (Layer 7)

🗺️ Architecture

🎉 What's New in v2.0

English

English

Overview

Quick Start

🚀 Fastest Way to Get Started

Installation

Run Demo

End-to-End Demo

v1.1 Features (New!)

🌐 Web Configuration UI

🔌 New Adapters

📊 Export Formats

📈 Advanced Analytics

v1.1 Features (New!)

🌐 Web Configuration UI

🔌 New Adapters

📊 Export Formats

📈 Advanced Analytics

Core Concepts

1. Data Model (Layer 2)

2. Metrics (Layer 4)

3. Bot Functional Tags (T1-T5)

4. Hub Score

Architecture

Usage

Basic Usage

Calculate Specific Metrics

Custom Data Adapter

API Reference

Testing

Performance

Known Limitations

Contributing

License

Authors

Acknowledgments

中文

概述

快速开始

安装

运行Demo

端到端Demo

核心概念

1. 数据模型（Layer 2）

2. 指标（Layer 4）

3. Bot功能标签（T1-T5）

4. Hub Score（中枢分数）

架构

使用方法

基本使用

计算特定指标

自定义数据适配器

API参考

测试

性能

已知限制

贡献

许可证

作者

致谢

v1.2 Features - External Data Loading

1. Inline Data Mode (Original)

2. External Data Mode (New!)

CLI Usage (v2.0)

Sync Command

Configuration

About

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