Log Analyzer Dashboard
PassAudited by ClawScan on May 1, 2026.
Overview
This appears to be a disclosed local Streamlit log dashboard; the main things to notice are local log-file access, a local web server, and unpinned Python dependencies.
This skill looks reasonable for local log analysis. Before installing, be comfortable running a local Streamlit dashboard and letting it read the log directory you choose. Logs can contain sensitive data, so point it only at intended folders, avoid unnecessary broad paths, and stop the dashboard when finished.
Findings (3)
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
If pointed at sensitive directories, the dashboard may display sensitive log contents locally.
The app enumerates and reads local files from a user-specified directory. This is central to log analysis, but users should be aware that logs can contain sensitive operational data, IPs, errors, or secrets.
dir_path = Path(directory) ... log_files.extend(dir_path.glob(pattern)) ... with open(log_file, 'r', encoding='utf-8', errors='ignore') as f:
Use it only on intended log folders, keep file-count limits narrow, and avoid analyzing logs that contain secrets unless you are comfortable viewing them in the local dashboard.
A local web dashboard will run on port 8506 while the skill is active.
The skill starts a local Streamlit web application. This is disclosed and purpose-aligned, but it is still a local process that remains running until stopped.
streamlit run log-analyzer.py --server.port 8506
Start it only when needed, access it via the local address, and stop the Streamlit process after use.
Future installs may receive different package versions than the author tested.
The skill documents installing standard Python packages by name, but without pinned versions or a lockfile in the provided artifacts.
"kind": "pip", "package": "streamlit" ... "package": "pandas" ... "package": "plotly"
Prefer a trusted Python environment and consider pinning package versions if you need reproducible or controlled installs.