Dynamic Dashboard with Sub-agent Spawning

Pain Point

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.

• Building dashboards takes weeks: By the time it's done, requirements have changed.
• Sequential polling is slow: Fetching from multiple APIs one-by-one is slow and hits rate limits.
• No alerting: Static dashboards don't tell you when something needs attention.
• Data silos: GitHub metrics, social signals, and system health live in completely different tools.

What You Can Do

• Sub-agent parallel fetching: Spawns sub-agents for each data source to avoid blocking and distribute API load.
• Unified dashboard: Aggregates results into a single formatted dashboard (text, HTML, or Canvas).
• Scheduled updates: Runs every N minutes with fresh data from all sources.
• Threshold alerts: Sends alerts when metrics cross defined thresholds.
• Historical trends: Maintains a metrics database for historical analysis and visualization.

Example Dashboard Sections

• GitHub: stars, forks, open issues, recent commits
• Social Media: Twitter mentions, sentiment analysis, Reddit discussions
• Markets: Polymarket volume, prediction trends
• System Health: CPU, memory, disk usage, service status

Skills You Need

• 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. Metrics Database

Set up a database to store historical metrics and alert configurations:

CREATE TABLE metrics (
  id SERIAL PRIMARY KEY,
  source TEXT, -- "github", "twitter", "polymarket", "system"
  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
);

2. Discord Channel

Create a Discord channel #dashboard for dashboard updates and alerts.

3. Configure the Pipeline

The dashboard runs on a 15-minute cron cycle:

1. Spawn sub-agents in parallel for each data source.
2. Fetch data from GitHub, Twitter, Polymarket, and system metrics.
3. Write results to the metrics database.
4. Aggregate all results and format a dashboard update.
5. Post to Discord #dashboard.
6. Check alert conditions and notify if thresholds are crossed.

4. Historical Queries

Query historical data: "Show me GitHub star growth over the past 30 days" or "When was the last Twitter sentiment spike?"

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

Key Insights

• Parallel > sequential: Sub-agents fetch data simultaneously, cutting dashboard update time from minutes to seconds.
• Alerts > dashboards: A dashboard you check is less useful than an alert that finds you.
• Historical context matters: Knowing that stars jumped 50 in an hour is more useful than knowing the current count.
• Start with 2 sources: Don't try to monitor everything at once — start with GitHub + system health, then add sources.

Related Links

• Parallel Processing with Sub-agents
• Dashboard Design Principles

Deploy with ShipClaw

Skip the setup — get a fully managed OpenClaw instance ready to run this use case.

Quick Start

1. Pick a plan — Pro recommended for this use case
2. Go to your Instances Dashboard and click Deploy New Instance
3. Once deployed, use the sample prompt above to configure your agent
4. Customize thresholds, schedules, and sources to fit your workflow