How can I see a summary of my agent's performance?

You can use the /metrics command within Claude Code or run the collector.py script directly from the plugin's script directory.

Why is tool call latency monitoring important?

Monitoring latency helps identify slow external integrations or inefficient tool logic that may be degrading the performance of your AI agent.

Can I monitor active user sessions?

Yes, the /sessions command provides a list of all active sessions, showing how long they have been running and how many messages have been exchanged.

What metrics does the Monitoring skill track?

It tracks agent lifecycle events, tool call success rates and latency, and session-specific data like duration and message counts.

KOR Agent Monitoring

Name: KOR Agent Monitoring
Author: felipepimentel

byfelipepimentel

分析与监控

Tracks and analyzes KOR agent performance, tool call metrics, and system health to optimize AI workflows.

关于

The Monitoring skill provides deep observability into KOR agent behavior, allowing developers to maintain high performance and reliability. It tracks critical data points including agent lifecycles, tool call latency, and success rates, while providing tools to list and manage active sessions. By offering both real-time commands and automated collection scripts, this skill enables proactive debugging and helps identify bottlenecks in AI agent interactions before they impact the end-user experience.

主要功能

Real-time agent lifecycle tracking (starts, ends, errors)
0 GitHub stars
Automated metrics summary generation via collector scripts
Performance benchmarking for AI agent optimization
Active session management and message count tracking
Detailed tool call analytics for latency and success rates

使用场景

Monitoring production AI agent health and success rates
Analyzing session volume and duration for capacity planning
Debugging failed tool calls and identifying latency bottlenecks

关于

主要功能

Real-time agent lifecycle tracking (starts, ends, errors)
0 GitHub stars
Automated metrics summary generation via collector scripts
Performance benchmarking for AI agent optimization
Active session management and message count tracking
Detailed tool call analytics for latency and success rates

使用场景

Monitoring production AI agent health and success rates
Analyzing session volume and duration for capacity planning
Debugging failed tool calls and identifying latency bottlenecks