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GitHubstar 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githubgh CLI用于 GitHub 指标birdTwitter用于社交数据web_search或web_fetch用于外部 APIpostgres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 );为仪表板更新创建一个 Discord 频道例如#dashboard。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 - GitHubstar 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。可选使用 Canvas 渲染带有图表和图表的 HTML 仪表板。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 SpawningStatic 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 visualizationPain PointBuilding a custom dashboard takes weeks. By the time its 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 DoesYou 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 commitsSocial Media: Twitter mentions, Reddit discussions, Discord activityMarkets: Polymarket volume, prediction trendsSystem Health: CPU, memory, disk usage, service statusSkills NeededSub-agent spawning for parallel executiongithub(gh CLI) for GitHub metricsbird(Twitter) for social dataweb_searchorweb_fetchfor external APIspostgresfor storing historical metricsDiscord or Canvas for rendering the dashboardCron jobs for scheduled updatesHow to Set it UpSet 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 );Create a Discord channel for dashboard updates (e.g., #dashboard).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.Optional: Use Canvas to render an HTML dashboard with charts and graphs.Query historical data: Show me GitHub star growth over the past 30 days.Related LinksParallel Processing with Sub-agentsDashboard Design Principles