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Trace Debuger

Verified

End-to-end trace debugging from trace_id. Fetch Jaeger trace and Elasticsearch logs, analyze possible bugs (optionally with local repository context), and ge...

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About This Skill

# Trace Debuger

Use this skill to generate a self-contained Markdown trace debug report.

Inputs

  • `trace_id` (required)
  • `jaeger_url` (optional, default `http://127.0.0.1:16686`)
  • `es_url` (optional, default `http://127.0.0.1:9200`)
  • `repo_path` (optional, absolute path, default `/Users/noodles/Desktop/code/go-components/examples/tracer`)
  • `output_path` (optional, default `./trace_debug_report_{trace_id}.md`)
  • `es_index` (optional, default `filebeat-tracer-*`)
  • `es_size` (optional, default `2000`)

Run

```bash python3 skills/trace_debuger/scripts/trace_debuger.py \ --trace-id <TRACE_ID> \ [--jaeger-url http://127.0.0.1:16686] \ [--es-url http://127.0.0.1:9200] \ [--repo-path /Users/noodles/Desktop/code/go-components/examples/tracer] \ [--output-path ./trace_debug_report_<TRACE_ID>.md] ```

Output

  • Writes Markdown report to `output_path`
  • MUST send the generated Markdown report to the user as a file attachment via the chat window in the same session before finishing the task
  • MUST send the report as ONE chat message only: attach the Markdown file and put the strict summary block in the same message caption/body.
  • `【markdown报告文件】` is a placeholder and MUST be replaced with the real uploaded Markdown filename (example: `trace_debug_report_<trace_id>.md`).
  • Use exactly this format in caption/body:

```text <真实markdown报告文件名> trace_id: xxxx status: xxx jaeger_url: xxx es_url: xxx 代码仓库路径:仓库路径 关键结论摘要:xxxx ```

  • Prints fixed summary lines to stdout:

```text trace_id: <trace_id> status: SUCCESS/FAIL jaeger_url: <jaeger_url> es_url: <es_url> 代码仓库路径:<repo_path|N/A> 关键结论摘要:<summary> ```

Notes

  • Keep logs sorted by timestamp ascending.
  • After fetching ES logs, run Codex in repository root (automated via `codex exec` equivalent to TUI paste workflow) with this prompt:
  • - `这是我的日志,请根据日志结合代码帮我排查分析bug,输出bug原因及解决方案,必须保持固定的格式。`
  • If repository is provided, include code-context hints and file matches for suspected bug areas.
  • If repository is not provided, base bug hypotheses on logs + spans only.
  • After analysis in chat workflow: send the generated Markdown report as a file attachment to the user through the chat window, with the strict summary block in the same message caption/body (single message only).
  • The first line must be the real Markdown filename (not placeholder text).
  • Finally, delete the local Markdown file.

Use Cases

  • Debug distributed system issues by fetching and analyzing Jaeger traces
  • Correlate trace spans with Elasticsearch logs using trace IDs
  • Identify performance bottlenecks across microservice call chains
  • Analyze possible bugs by combining trace data with local repository context
  • Generate end-to-end debugging reports from distributed trace data

Pros & Cons

Pros

  • +Combines Jaeger traces and Elasticsearch logs for comprehensive debugging
  • +Optional local repository context adds code-level insight to trace analysis
  • +End-to-end approach from trace ID to bug hypothesis

Cons

  • -Requires Jaeger and Elasticsearch infrastructure already in place
  • -Specific toolchain dependency limits use with other observability stacks

FAQ

What does Trace Debuger do?
End-to-end trace debugging from trace_id. Fetch Jaeger trace and Elasticsearch logs, analyze possible bugs (optionally with local repository context), and ge...
What platforms support Trace Debuger?
Trace Debuger is available on Claude Code, OpenClaw.
What are the use cases for Trace Debuger?
Debug distributed system issues by fetching and analyzing Jaeger traces. Correlate trace spans with Elasticsearch logs using trace IDs. Identify performance bottlenecks across microservice call chains.

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