Splunk Log Analyzer Dashboard
v1.0.1纯本地日志分析系统，支持日志统计、重复检测、错误分析和异常识别
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byGodYoung@godyounger
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (local log analysis) matches what is provided: a Streamlit app (log-analyzer.py) and a small start script. Declared required binary (streamlit) and Python libraries (streamlit, pandas, plotly) are exactly what the app needs; there are no unrelated credentials, binaries, or config paths requested.
Instruction Scope
SKILL.md instructs launching the Streamlit app and describes only local operations (selecting a log directory, file patterns, UI options). The code reads and parses local files (user-specified directories) which is expected for this purpose. There are no instructions or code paths that reference external endpoints, environment secrets, or unrelated system configuration.
Install Mechanism
There is no registry-level install spec; the SKILL.md includes recommended pip installs for streamlit, pandas, and plotly. Those are reasonable and proportionate. The package sources are standard Python packages (pip) and no arbitrary downloads or archive extraction are present.
Credentials
The skill declares no required environment variables or credentials and the code does not reference environment secrets. It only needs local filesystem access to read log files, which is consistent with its purpose.
Persistence & Privilege
always is false and the skill is user-invocable. It does not request elevated privileges, does not modify other skills or global agent settings, and does not persist credentials. Running creates only a local Streamlit server process.
Assessment
This skill appears to do what it says: run a local Streamlit dashboard to analyze log files. Before installing/running: (1) review the included log-analyzer.py yourself (it is provided) to confirm no unexpected network calls or data exfiltration; (2) run it as a non‑privileged user and avoid pointing it at sensitive system directories unless you intend to examine those logs; (3) run dependencies in a virtualenv or container to avoid polluting system Python; (4) the Streamlit server listens on a port—ensure the host binding/ firewall settings prevent exposing the UI to untrusted networks (only expose to localhost if you want it local-only). If you need higher assurance, run the skill in an isolated container and test with non-sensitive sample logs first.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.
Runtime requirements
📊 Clawdis
Binsstreamlit