- name
- claw-reliability
- description
- Agent observability — monitors tool invocations, LLM calls, token usage, costs, and anomalies with pluggable alerts and a real-time dashboard.
- metadata
- {"openclaw": {"requires": {"bins": ["python3"], "config": ["agents.defaults.workspace"]}, "os": ["linux", "darwin"]}}
Claw Reliability — Agent Observability Skill
You are an AI agent with observability capabilities. Use this skill to monitor, analyze, and report on agent behavior.
When to use this skill
- When the user asks to monitor agent activity, check agent health, or review agent metrics
- When the user asks about tool usage, failure rates, costs, or token consumption
- When the user asks to set up alerts or check for anomalies
- When the user asks for a reliability report or dashboard
Available commands
Start monitoring
Run the monitoring daemon to begin collecting metrics:
cd {baseDir} && python3 scripts/monitor.py start --config {baseDir}/config.yamlShow metrics summary
Display current metrics for the active session or all sessions:
cd {baseDir} && python3 scripts/monitor.py summaryShow tool report
Display tool invocation success/failure rates:
cd {baseDir} && python3 scripts/monitor.py toolsShow cost report
Display token usage and cost projections:
cd {baseDir} && python3 scripts/monitor.py costsCheck for anomalies
Run anomaly detection on recent activity:
cd {baseDir} && python3 scripts/monitor.py anomaliesList alerts
Show recent alerts and their severity:
cd {baseDir} && python3 scripts/monitor.py alertsConfigure alert destination
Set up where alerts are sent (Discord, Slack, log file, etc.):
cd {baseDir} && python3 scripts/monitor.py configure-alerts --destination discord --webhook-url <URL>Launch dashboard
Start the FastAPI + React dashboard for visual monitoring:
cd {baseDir} && python3 dashboard/backend/main.pyThen open http://localhost:8777 in a browser.
How metrics are collected
This skill reads OpenClaw gateway events and session transcripts to extract:
- Tool invocations: tool name, success/fail, duration, arguments
- LLM calls: model, tokens in/out, latency, estimated cost
- Session lifecycle: start/end times, message counts
- Anomalies: repeated failures, cost spikes, loop detection
All data is stored in a local SQLite database at {baseDir}/data/metrics.db.
Alert thresholds (defaults, configurable)
- Tool failure: 3+ consecutive errors on the same tool
- Cost spike: Token spend exceeds 2x the rolling 1-hour average
- Loop detection: Same tool called 10+ times in a single agent turn
- Unusual activity: Tool called that has never been used before in this agent's history
Notes
- This skill does NOT send data externally unless you configure an alert destination
- All metrics stay local in SQLite
- The dashboard runs on localhost only by default