使用子智能体生成的动态仪表板

静态仪表板显示过时的数据，需要持续手动更新。你希望跨多个数据源获得实时可见性，而无需构建自定义前端或触及 API 速率限制。

此工作流程创建一个实时仪表板，生成子智能体以并行获取和处理数据：

• 同时监控多个数据源（API、数据库、GitHub、社交媒体）

• 为每个数据源生成子智能体，以避免阻塞并分散 API 负载

• 将结果聚合到统一的仪表板中（文本、HTML 或 Canvas）

• 每 N 分钟用新数据更新一次

• 当指标超过阈值时发送警报

• 在数据库中维护历史趋势以便可视化

痛点

构建自定义仪表板需要数周时间。等到完成时，需求已经改变。顺序轮询多个 API 速度慢且容易触及速率限制。你需要即时洞察，而不是在周末编码之后。

功能描述

你可以通过对话定义你想要监控的内容：“追踪 GitHub star 数、Twitter 提及、Polymarket 交易量和系统健康状况。”OpenClaw 会生成子智能体以并行获取每个数据源，聚合结果，并将格式化的仪表板发送到 Discord 或生成 HTML 文件。更新任务通过 cron 计划自动运行。

示例仪表板部分：

• GitHub：star 数、fork 数、未解决问题、最近提交

• 社交媒体：Twitter 提及、Reddit 讨论、Discord 活动

• 市场：Polymarket 交易量、预测趋势

• 系统健康状况：CPU、内存、磁盘使用率、服务状态

所需技能

• 子智能体生成以支持并行执行

• github（gh CLI）用于 GitHub 指标

• bird（Twitter）用于社交数据

• web_search 或 web_fetch 用于外部 API

• postgres 用于存储历史指标

• Discord 或 Canvas 用于渲染仪表板

• Cron 作业用于计划更新

如何设置

1. 设置指标数据库：

CREATE TABLE metrics (
  id SERIAL PRIMARY KEY,
  source TEXT, -- 例如：“github”、“twitter”、“polymarket”
  metric_name TEXT,
  metric_value NUMERIC,
  timestamp TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE alerts (
  id SERIAL PRIMARY KEY,
  source TEXT,
  condition TEXT,
  threshold NUMERIC,
  last_triggered TIMESTAMPTZ
);

1. 为仪表板更新创建一个 Discord 频道（例如，#dashboard）。

2. 提示 OpenClaw：

你是我的动态仪表板管理器。每 15 分钟运行一次 cron 作业：

1. 并行生成子智能体以从以下来源获取数据：
   - GitHub：star 数、fork 数、未解决问题、提交（过去 24 小时）
   - Twitter：提及“@username”的情况，情感分析
   - Polymarket：跟踪市场的交易量
   - 系统：通过 shell 命令获取 CPU、内存、磁盘使用率

2. 每个子智能体将结果写入指标数据库。

3. 聚合所有结果并格式化仪表板：

📊 **仪表板更新** — [时间戳]

**GitHub**
- ⭐ Star 数：[数量]（+[变化]）
- 🍴 Fork 数：[数量]
- 🐛 未解决问题：[数量]
- 💻 提交（24 小时）：[数量]

**社交媒体**
- 🐦 Twitter 提及：[数量]
- 📈 情感：[积极/消极/中性]

**市场**
- 📊 Polymarket 交易量：[金额] 美元
- 🔥 趋势：[市场名称]

**系统健康状况**
- 💻 CPU：[使用率]%
- 🧠 内存：[使用率]%
- 💾 磁盘：[使用率]%

4. 发布到 Discord #dashboard 频道。

5. 检查警报条件：
   - 如果 GitHub star 数在 1 小时内变化 > 50 → 通知我
   - 如果系统 CPU > 90% → 警报
   - 如果 Twitter 负面情感激增 → 通知

将所有指标存储在数据库中以便历史分析。

1. 可选：使用 Canvas 渲染带有图表和图表的 HTML 仪表板。

2. 查询历史数据：“显示过去 30 天 GitHub star 增长情况。”

相关链接

• 使用子智能体进行并行处理

• 仪表板设计原则

原文

Dynamic Dashboard with Sub-agent Spawning

Static dashboards show stale data and require constant manual updates. You want real-time visibility across multiple data sources without building a custom frontend or hitting API rate limits.

This workflow creates a live dashboard that spawns sub-agents to fetch and process data in parallel:

• Monitors multiple data sources simultaneously (APIs, databases, GitHub, social media) • Spawns sub-agents for each data source to avoid blocking and distribute API load • Aggregates results into a unified dashboard (text, HTML, or Canvas) • Updates every N minutes with fresh data • Sends alerts when metrics cross thresholds • Maintains historical trends in a database for visualization

Pain Point

Building a custom dashboard takes weeks. By the time it's done, requirements have changed. Polling multiple APIs sequentially is slow and hits rate limits. You need insight now, not after a weekend of coding.

What It Does

You define what you want to monitor conversationally: "Track GitHub stars, Twitter mentions, Polymarket volume, and system health." OpenClaw spawns sub-agents to fetch each data source in parallel, aggregates the results, and delivers a formatted dashboard to Discord or as an HTML file. Updates run automatically on a cron schedule.

Example dashboard sections:

• GitHub: stars, forks, open issues, recent commits

• Social Media: Twitter mentions, Reddit discussions, Discord activity

• Markets: Polymarket volume, prediction trends

• System Health: CPU, memory, disk usage, service status

Skills Needed

• Sub-agent spawning for parallel execution

• github (gh CLI) for GitHub metrics

• bird (Twitter) for social data

• web_search or web_fetch for external APIs

• postgres for storing historical metrics

• Discord or Canvas for rendering the dashboard

• Cron jobs for scheduled updates

How to Set it Up

1. Set up a metrics database:

CREATE TABLE metrics (
  id SERIAL PRIMARY KEY,
  source TEXT, -- e.g., "github", "twitter", "polymarket"
  metric_name TEXT,
  metric_value NUMERIC,
  timestamp TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE alerts (
  id SERIAL PRIMARY KEY,
  source TEXT,
  condition TEXT,
  threshold NUMERIC,
  last_triggered TIMESTAMPTZ
);

1. Create a Discord channel for dashboard updates (e.g., #dashboard).

2. Prompt OpenClaw:

You are my dynamic dashboard manager. Every 15 minutes, run a cron job to:

1. Spawn sub-agents in parallel to fetch data from:
   - GitHub: stars, forks, open issues, commits (past 24h)
   - Twitter: mentions of "@username", sentiment analysis
   - Polymarket: volume for tracked markets
   - System: CPU, memory, disk usage via shell commands

2. Each sub-agent writes results to the metrics database.

3. Aggregate all results and format a dashboard:

📊 **Dashboard Update** — [timestamp]

**GitHub**
- ⭐ Stars: [count] (+[change])
- 🍴 Forks: [count]
- 🐛 Open Issues: [count]
- 💻 Commits (24h): [count]

**Social Media**
- 🐦 Twitter Mentions: [count]
- 📈 Sentiment: [positive/negative/neutral]

**Markets**
- 📊 Polymarket Volume: $[amount]
- 🔥 Trending: [market names]

**System Health**
- 💻 CPU: [usage]%
- 🧠 Memory: [usage]%
- 💾 Disk: [usage]%

4. Post to Discord #dashboard.

5. Check alert conditions:
   - If GitHub stars change > 50 in 1 hour → ping me
   - If system CPU > 90% → alert
   - If negative sentiment spike on Twitter → notify

Store all metrics in the database for historical analysis.

1. Optional: Use Canvas to render an HTML dashboard with charts and graphs.

2. Query historical data: "Show me GitHub star growth over the past 30 days."

Related Links

• Parallel Processing with Sub-agents

• Dashboard Design Principles