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Skill Health Monitor

Monitor deployed skills for performance drift, errors, and unexpected behavior changes. Continuous post-deployment health checks with alerting and trend trac...

personAuthor: trypto1019hubclawhub
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Skill Health Monitor

Catch skill degradation before it becomes a crisis. Monitors response times, error rates, output drift, and resource usage for deployed skills.

Why This Exists

Skills work fine during testing, then silently degrade in production. Free models change behavior, APIs add latency, memory leaks accumulate. By the time you notice, your agent has been running on broken skills for hours.

Commands

Monitor a skill execution

python3 {baseDir}/scripts/health_monitor.py check --skill <name> --cmd "python3 path/to/script.py"

View health dashboard

python3 {baseDir}/scripts/health_monitor.py dashboard

Set alert thresholds

python3 {baseDir}/scripts/health_monitor.py threshold --skill <name> --max-latency 5000 --max-errors 3

Export health report

python3 {baseDir}/scripts/health_monitor.py report --json

View trends for a skill

python3 {baseDir}/scripts/health_monitor.py trend --skill <name> --period 24h

What It Tracks

  • Latency: Execution time per invocation, p50/p95/p99 percentiles
  • Error rate: Failed executions, error types, frequency
  • Output drift: Detects when output format or content changes unexpectedly
  • Resource usage: Memory and CPU at execution time
  • Uptime: Availability over time windows (1h, 24h, 7d)

Alerting

  • Console alerts when thresholds are exceeded
  • JSON webhook support for external integrations
  • Configurable per-skill thresholds

Data Storage

Health data is stored in ~/.openclaw/health/ as JSON files. One file per skill, rotated daily.