trace

Visualize agent execution timelines and aggregate statistics from MCP trace data.

Updated Aug 27, 2026

Search Tags:#performance #debugging #timeline #visualization #trace #agent flow

Ready to run

npx skills add https://github.com/mrsono0/lectures --skill trace-mrsono0

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a clear, chronological view of how an AI agent executed a task, detailing every interaction and decision point.

Core Features & Use Cases

Timeline Visualization: See the sequence of hooks, keywords, skills, agents, and tools used.
Performance Analysis: Identify bottlenecks and understand tool performance.
Flow Pattern Recognition: Understand the chains of events from keyword detection to skill activation and tool usage.
Use Case: After a complex query, use this Skill to understand exactly why the agent chose certain tools or skills, helping to debug unexpected outcomes or optimize future interactions.

Quick Start

Show me the trace timeline for the last session.

Frequently Asked Questions about trace

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I visualize an agent execution flow to debug performance bottlenecks?▼

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.

What is the best way to analyze an AI agent's chronological event timeline?▼

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.

Do I need specific MCP tools to trace agent execution patterns?▼

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.

Can I identify performance bottlenecks in tool usage after a complex query?▼

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.

When do I need to trace the timeline of agent interactions?▼

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.