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trace

Visualize agent execution timelines and aggregate statistics via MCP tools.

20|6|Updated Feb 13, 2026
Search Tags:#performance#debugging#timeline#visualization#trace#agent flow
One-click install
npx skills add https://github.com/sigridjineth/oh-my-codex --skill trace-sigridjineth
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: trace
Source: https://github.com/sigridjineth/oh-my-codex/tree/main/skills/trace
Command: npx skills add https://github.com/sigridjineth/oh-my-codex --skill trace-sigridjineth

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a clear, chronological view of how an AI agent executed a task, detailing the interactions between different components like hooks, skills, agents, and tools.

Core Features & Use Cases

  • Timeline Visualization: See the exact sequence of events during an agent's operation.
  • Performance Analysis: Identify bottlenecks and understand tool/agent performance.
  • Debugging: Quickly diagnose issues by tracing the execution path.
  • Use Case: After a complex multi-step task, use this Skill to review the agent's journey, understand why certain decisions were made, and pinpoint any inefficiencies.

Quick Start

Use the trace skill to show the latest session's event timeline and summary.

Frequently Asked Questions about trace

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

FAQPage Schema
How do I visualize an AI agent's execution timeline for debugging?▼

Visualizing an agent execution timeline for debugging involves mapping the chronological sequence of events during an operation. This process tracks interactions between hooks, skills, agents, and tools to pinpoint inefficiencies.

What is the best way to analyze agent performance bottlenecks and tool interactions?▼

Analyzing agent performance bottlenecks is best achieved by reviewing the chronological event timeline and aggregate statistics of the execution flow. This approach identifies specific tool interactions and performance delays within the agent flow.

How can I trace the chronological event flow of my AI agent's operations?▼

Tracing the chronological event flow of an AI agent involves generating a timeline that details the exact sequence of operations. This allows you to review the agent's journey and understand why certain decisions were made during a task.

Do I need specific MCP tools to get detailed flow and bottleneck analysis?▼

Yes, you need the specific MCP tools `trace_timeline` and `trace_summary` to perform detailed flow and bottleneck analysis. These tools provide the necessary visualization and aggregate statistics for debugging the agent's execution path.

Can I diagnose why my AI agent made certain decisions during a complex multi-step task?▼

Yes, you can diagnose why an AI agent made certain decisions during a complex multi-step task by tracing its execution path. Visualizing the chronological timeline reveals the interactions that led to specific outcomes and highlights inefficiencies.

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