Visualize agent flow timelines and aggregate session statistics.
npx skills add https://github.com/chrisxue90/oh-my-qwencode --skill trace-chrisxue90
Please help me install this Agent Skill. Skill: trace Source: https://github.com/chrisxue90/oh-my-qwencode/tree/main/skills/trace Command: npx skills add https://github.com/chrisxue90/oh-my-qwencode --skill trace-chrisxue90
Show agent flow trace timeline and summary to aid debugging and auditing by exposing how hooks, keywords, skills, agents, and tools interact over a session.
Invoke trace_timeline to view the latest session timeline, then run trace_summary to see aggregate statistics.
High-intent search queries and answers about installing and using this skill.
To trace agent workflow execution, run trace_timeline to view a chronological sequence of events for the latest session. This reveals how hooks, skills, agents, and tools interact, helping you identify execution bottlenecks and debug complex workflows.
An agent flow timeline is a chronological visualization of session events showing interactions between hooks, keywords, skills, agents, and tools. You need it when auditing complex workflows or debugging performance issues to understand execution patterns and isolate bottlenecks.
Yes, you can narrow the timeline output by filtering specific event types including hooks, keywords, skills, agents, tools, and modes. This filtering allows you to target specific analysis and focus on particular aspects of the workflow execution.
Run trace_summary to get aggregate statistics for agent sessions. This provides metrics including hook fire counts, detected keywords, activated skills, mode transitions, and tool performance, enabling focused analysis of your workflow execution.
No, the agent flow tracer operates without any dependencies or external components. It is a standalone skill that directly analyzes session data to present timeline traces and aggregate statistics for debugging and auditing purposes.
The best way to audit mode transitions and skill activations is to use trace_summary, which aggregates these metrics across a session. Pair it with trace_timeline to view the chronological context of when these transitions and activations occur.
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