Skill flagged — suspicious patterns detected
ClawHub Security flagged this skill as suspicious. Review the scan results before using.
Trace Debugger Safety
v1.0.0Safer end-to-end trace debugging from trace_id using Jaeger and Elasticsearch with guarded Codex analysis. Use when a user wants a trace report similar to tr...
⭐ 0· 47·0 current·0 all-time
MIT-0
Download zip
LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Suspicious
medium confidencePurpose & Capability
Name/description (trace debugging using Jaeger + Elasticsearch, optional code context) aligns with the script: it queries Jaeger/ES endpoints, normalizes spans/logs, and performs optional repository analysis. Requesting repo_path and jaeger/es URLs is coherent with the stated purpose.
Instruction Scope
SKILL.md instructs running the included Python script, providing trace_id and optional jaeger/es URLs and repo path. The instructions explicitly allow reading a local repo (if provided) and require writing and then deleting a Markdown report — those are within scope. The skill also instructs sending the generated report as a file attachment with a specific message format; that is an agent-level behavior but consistent with the skill. One scope concern: the SKILL.md and code emphasize an optional 'Codex' analysis path; the docs warn about prompt-injection risk, but they do not explain what 'Codex' binary/service is or what credentials it needs.
Install Mechanism
No install spec — instruction-only with an included Python script. There are no downloads or package installs, so nothing extra is written to disk by an installer. Running the script will execute code bundled with the skill (normal for instruction-plus-script skills).
Credentials
The skill declares no environment variables or credentials, but the code imports subprocess and includes a run_codex_analysis flow (truncated in the provided snippet) that likely invokes an external 'Codex' analysis step. If Codex requires API keys (e.g., OpenAI/OpenAI CLI, other LLM service), those credentials are not declared in requires.env or documented, creating a mismatch. Additionally, the script reads arbitrary files under a provided absolute repo_path, which is expected for code-context features but is privileged file access — users should only supply trusted repo paths.
Persistence & Privilege
The skill is not always-included and does not request persistent system privileges. It runs only when invoked and does not appear to modify other skills or system-wide agent settings. Writing a temporary Markdown report and deleting it afterward is normal and explicitly documented.
What to consider before installing
This skill is mostly coherent for trace debugging, but exercise caution before installing or running it:
- Inspect how run_codex_analysis is implemented (the script references a 'Codex' analysis path). Confirm whether it calls an external CLI or LLM API and whether that requires API keys (e.g., OPENAI_API_KEY or a custom token). The skill declares no required env vars, so missing credential documentation is a gap.
- Prefer running with --no-codex when analyzing logs from untrusted sources to avoid prompt-injection risks (the SKILL.md already advises this).
- Only pass an absolute repo_path you trust; the script will read many source files and may include file paths in the report.
- If you need to run this in production, run it against local/test Jaeger/ES endpoints first (defaults are localhost). Verify network requests made by the script (which endpoints are contacted) and confirm there are no hardcoded remote endpoints.
- If you are not comfortable auditing the full run_codex_analysis code path or any subprocess calls the script makes, consider not enabling Codex analysis or running the script in a restricted environment (container, limited filesystem access) to reduce risk.Like a lobster shell, security has layers — review code before you run it.
latestvk970pvq4skh23rb27n3sahxbmx83k4kb
License
MIT-0
Free to use, modify, and redistribute. No attribution required.