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Trace Debuger
作者
gakkiismywife
· GitHub ↗
· v0.2.1
461
总下载
1
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0
当前安装
3
版本数
在 OpenClaw 中安装
/install trace-debuger
功能描述
End-to-end trace debugging from trace_id. Fetch Jaeger trace and Elasticsearch logs, analyze possible bugs (optionally with local repository context), and ge...
安全使用建议
Before installing or running this skill: (1) Understand it will read any files under the provided repo_path and send logs+code to an external 'codex' tool invoked by subprocess — confirm what 'codex' does and where it sends data. (2) The skill metadata does not declare the 'codex' CLI as a required binary or any auth variables for ES/Jaeger — expect manual setup or failures. (3) Avoid providing paths to sensitive repositories; run first in a sandbox or VM with limited network access. (4) If you need to use it with private Jaeger/ES, verify how credentials are supplied and that you trust the external analysis service. (5) Consider inspecting or running the included script locally to confirm behavior (and remove or change the hard-coded example repo_path) before granting it access to real data.
功能分析
Type: OpenClaw Skill
Name: trace-debuger
Version: 0.2.1
The skill performs trace debugging by fetching data from Jaeger and Elasticsearch and analyzing local source code. It exhibits high-risk behaviors including recursive file system traversal and reading of local files within a user-defined 'repo_path' (scripts/trace_debuger.py), and it executes an external binary 'codex' via subprocess.run to perform analysis. While these actions align with the stated purpose of debugging code, the broad file access and execution of external commands represent a significant attack surface, especially given the hardcoded default path '/Users/noodles/...' which suggests unvetted development code.
能力评估
Purpose & Capability
The name/description (trace debugging via Jaeger + Elasticsearch + optional repository context) matches the script's behavior. However, the SKILL.md and script expect an external 'codex' analysis step (runs a codex CLI/subprocess) and access to local repository files, yet the registry metadata declares no required binaries or environment variables. The default repo_path in SKILL.md points to a specific absolute user path (/Users/noodles/...) which is unusual for a generic skill and may be a leftover from development.
Instruction Scope
SKILL.md instructs the agent to run the included Python script, fetch traces from jaeger_url and logs from es_url, and optionally scan a local repo. It also mandates that the generated Markdown file be sent to the user as ONE chat message with a strict caption format and then deleted locally. That strict single-message upload + deletion step could be used to obfuscate data transfer and reduces auditability. The instructions instruct running 'codex exec' (or equivalent) against repository and logs, which will send code/log data to an external tool/service.
Install Mechanism
There is no install spec (instruction-only), which minimizes disk writes. However, the runtime flow relies on an external CLI ('codex') invoked via subprocess. The skill metadata does not declare this required binary—this mismatch is a practical omission (the skill will fail or behave differently if codex is not present).
Credentials
The skill declares no required environment variables, but it will access network endpoints (jaeger_url, es_url) and arbitrary local files under the provided repo_path. If the codex CLI sends data to an external service, analysis results and repository contents could leave the host. The default absolute repo_path is a red flag (points to a particular user's Desktop). No authentication handling for ES/Jaeger is declared (these services often require credentials), so users might supply credentials ad hoc or the script might be run against local, unauthenticated endpoints.
Persistence & Privilege
The skill does not request permanent presence (always:false) and does not modify other skill configs. It writes a local Markdown file and explicitly instructs deletion afterwards; the deletion behavior itself is not privileged but combined with the one-message upload requirement reduces leftover artifacts for inspection.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install trace-debuger - 安装完成后，直接呼叫该 Skill 的名称或使用
/trace-debuger触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v0.2.1
Enforce single-message delivery: markdown report as file attachment + strict summary format with real filename.
v0.2.0
Integrate Codex log+code analysis, remove local rule-based bug analysis, keep fixed response format and chat file delivery workflow.
v0.1.0
Initial release: Jaeger+ES trace debugging with markdown report, fixed reply format, repo-path default, code-line evidence via caller mapping.
元数据
常见问题
Trace Debuger 是什么？
End-to-end trace debugging from trace_id. Fetch Jaeger trace and Elasticsearch logs, analyze possible bugs (optionally with local repository context), and ge... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 461 次。
如何安装 Trace Debuger？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install trace-debuger」即可一键安装，无需额外配置。
Trace Debuger 是免费的吗？
是的，Trace Debuger 完全免费（开源免费），可自由下载、安装和使用。
Trace Debuger 支持哪些平台？
Trace Debuger 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Trace Debuger？
由 gakkiismywife（@gakkiismywife）开发并维护，当前版本 v0.2.1。
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