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trace-qa

Analyze agent execution traces to inspect steps, tool usage, and LLM calls.

1.1k|99|Updated Feb 12, 2026
Search Tags:#debugging#qa#llm#trace#performance analysis#agent execution
One-click install
npx skills add https://github.com/dp-archive/archive --skill trace-qa-dp-archive
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: trace-qa
Source: https://github.com/dp-archive/archive/tree/main/seed_skills/trace-qa
Command: npx skills add https://github.com/dp-archive/archive --skill trace-qa-dp-archive

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps you understand and debug agent execution traces, making it easier to identify issues, analyze performance, and understand agent behavior.

Core Features & Use Cases

  • Trace Analysis: Get an overview of trace status, duration, and token usage.
  • Step-by-Step Debugging: Examine individual steps, tool calls, and LLM interactions within a trace.
  • Efficiency Analysis: Analyze token usage and identify performance bottlenecks.
  • Use Case: When an agent run fails, use this skill to pinpoint the exact step where the error occurred and inspect the inputs and outputs.

Quick Start

Use the trace-qa skill to get an overview of the trace with ID 'trace_123'.

Frequently Asked Questions about trace-qa

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

FAQPage Schema
How do I debug an agent execution trace to find where a run failed?▼

To debug an agent execution trace, you fetch the execution data using a specific trace ID to examine individual steps. This pinpoints the exact point of failure and reveals the specific tool usage and LLM interactions involved.

What is agent trace analysis and when do I need it?▼

Agent trace analysis is the process of examining execution data to understand agent behavior and identify issues. You need it when an agent run fails or exhibits unexpected performance bottlenecks that require detailed inspection of steps and tool usage.

Can I analyze token usage and performance bottlenecks within an LLM trace?▼

Yes, you can analyze token usage and identify performance bottlenecks within an LLM trace. By processing the trace ID, you get an overview of trace status, duration, and total token consumption to locate efficiency issues.

Do I need a trace ID to inspect agent steps and tool usage?▼

Yes, a trace ID is required to fetch and process execution data for agent debugging. The trace ID retrieves the specific execution record so you can inspect individual steps, tool usage, and LLM calls within that trace.

What's the best way to understand agent behavior after an unsuccessful run?▼

The best way to understand agent behavior after an unsuccessful run is to perform step-by-step debugging on the execution trace. This involves examining individual tool calls and LLM interactions to see exactly how the agent progressed through its tasks.

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