Trace Debuger
VerifiedEnd-to-end trace debugging from trace_id. Fetch Jaeger trace and Elasticsearch logs, analyze possible bugs (optionally with local repository context), and ge...
$ Add to .claude/skills/ 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
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