Diagnose AI system health by monitoring metrics and detecting anomalies.
npx skills add https://github.com/blackplume233/Actant --skill self-diagnosis
Please help me install this Agent Skill. Skill: self-diagnosis Source: https://github.com/blackplume233/Actant/tree/main/examples/actant-hub/skills/self-diagnosis Command: npx skills add https://github.com/blackplume233/Actant --skill self-diagnosis
Monitors AI agent ecosystems to detect health issues early, identify anomalies, and locate root causes before they cause outages.
Run a health-check pass across active agents and report any detected anomalies.
High-intent search queries and answers about installing and using this skill.
To monitor AI agent ecosystems, collect metrics across Daemon and Agent components to detect anomalies and locate root causes before outages. This involves tracking CPU, memory, disk, and latency to trigger proactive alerts.
The best way to trace root causes for high-latency tasks is dependency-chain tracing, which maps failures from Daemon components to external services. This localizes faults across logs and MCP traces.
Yes, anomaly detection enables proactive alerting on AI system health by identifying patterns like spikes, drifts, cascades, and recurrences. This ensures early detection of performance degradation in production agents.
Log analysis works for error extraction and hotspot identification by examining MCP traces and system logs. It helps surface specific failure points and map them across the agent dependency chain.
Run a health-check pass by applying guided diagnostics across active agents to collect system metrics and analyze logs. This reports detected anomalies and pinpoints the root causes of failures.
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