Visualize agent execution timelines and aggregate statistics from MCP trace data.
npx skills add https://github.com/mrsono0/lectures --skill trace-mrsono0
Please help me install this Agent Skill. Skill: trace Source: https://github.com/mrsono0/lectures/tree/main/.claude/skills/trace Command: npx skills add https://github.com/mrsono0/lectures --skill trace-mrsono0
This Skill provides a clear, chronological view of how an AI agent executed a task, detailing every interaction and decision point.
Show me the trace timeline for the last session.
High-intent search queries and answers about installing and using this skill.
To visualize agent execution flow, this Skill generates a chronological timeline of hooks, keywords, skills, agents, and tools, helping you identify performance bottlenecks and debug unexpected outcomes.
Analyzing an agent's chronological event timeline involves tracking interactions like hooks and tool usage to recognize flow patterns, which reveals exactly why an agent chose certain tools or skills during a complex query.
Yes, you need the MCP tools `trace_timeline` and `trace_summary` to perform detailed session analysis and retrieve aggregate statistics for visualizing the agent's execution flow.
Yes, you can identify performance bottlenecks in tool usage by analyzing the aggregate statistics and chronological event timeline, which detail every interaction and decision point from keyword detection to skill activation.
You need to trace the timeline of agent interactions after a complex query when you want to debug unexpected outcomes, optimize future interactions, or understand the exact chains of events leading to skill activation.
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