trace-qa
GitHub用于分析智能体执行轨迹，支持调试失败运行、理解行为逻辑及评估Token效率。通过标准化命令获取概览与详情，帮助用户快速定位问题并审查Agent执行情况。
Trigger Scenarios
Install
npx skills add dp-archive/archive --skill trace-qa -g -y
SKILL.md
Frontmatter
{
"name": "trace-qa",
"description": "Analyze and answer questions about agent execution traces. Use this skill when the user asks about a trace, wants to debug a failed agent run, understand what an agent did, analyze token usage or efficiency, or asks \"what happened in trace X\". Triggers: trace analysis, trace debugging, trace QA, execution review, agent run review."
}
Trace QA
Analyze agent execution traces to answer questions about what happened, why it failed, how efficient it was, or any other aspect of the run.
Workflow
Always start with overview to understand the trace before diving into details.
1. Get the overview first
python scripts/fetch_trace.py <trace_id> overview
This returns metadata (status, duration, tokens, model) and summaries (request, answer preview, tool usage counts). Use this to orient yourself before going deeper.
2. Explore steps or LLM calls as needed
Depending on the user's question, drill into the relevant data:
| User wants to know... | Command |
|---|---|
| What tools were called and in what order | steps [start] [count] |
| Full input/output of a specific tool call | step <N> |
| How many LLM calls and their token costs | llm-calls [start] [count] |
| What messages were sent to Claude in a specific turn | llm-call <N> |
| Just the final result | answer |
3. Handle long content with segmented reads
When content is large, the script automatically segments output to ~4000 characters.
If you see a [CONTINUED: ...] message at the end of output, call the command shown
in that message to read the next segment. Repeat until all content is read.
Example sequence:
python scripts/fetch_trace.py <id> step 5
# Output ends with: [CONTINUED: use 'step 5 --offset 4000' for next segment]
python scripts/fetch_trace.py <id> step 5 --offset 4000
# Output ends with: [CONTINUED: use 'step 5 --offset 8000' for next segment]
python scripts/fetch_trace.py <id> step 5 --offset 8000
# Full content now read
Command Reference
| Mode | Syntax | Description |
|---|---|---|
overview |
fetch_trace.py <id> overview |
Metadata + summary stats |
steps |
fetch_trace.py <id> steps [start] [count] |
Paginated step list (default: 30/page) |
step |
fetch_trace.py <id> step <N> [--offset <chars>] |
Single step full content |
llm-calls |
fetch_trace.py <id> llm-calls [start] [count] |
Paginated LLM call list |
llm-call |
fetch_trace.py <id> llm-call <N> [--offset <chars>] |
Single LLM call full content |
answer |
fetch_trace.py <id> answer |
Final answer only |
Common Analysis Patterns
Failure diagnosis: overview → find error → steps list → examine failing step detail
Token efficiency: overview (total tokens) → llm-calls list (per-call breakdown) → identify expensive calls
Behavior understanding: overview → steps list → step details for key tool calls
Tool usage audit: overview (tool summary) → steps list filtered by tool name
Environment
Set API_BASE_URL to override the default API endpoint (http://127.0.0.1:62610).
Version History
- f8bf8ca Current 2026-08-20 08:34
