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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (article quality/AI-detection/originality) match the included modules: classifier, analyzers, AI detector, scorer, and report generator. Declared runtime needs (none) are minimal; the requirements.txt (jieba, scikit-learn, numpy) are appropriate for Chinese text analysis and NLP features the skill implements.
Instruction Scope
SKILL.md and main.py limit operations to analyzing provided text, optional file input, and batch directory processing. Instructions do not ask the agent to read unrelated system files, secrets, or to transmit data to external endpoints. The only file I/O is reading user-supplied article files (via --file or --dir), which is expected behavior for this tool.
Install Mechanism
No install specification is provided even though the skill includes Python modules and a requirements.txt. This is not a security risk by itself, but it means dependencies won't be automatically installed; a runtime environment must provide Python and the listed packages. There are no downloads or remote installers in the manifest.
Credentials
The skill requests no environment variables, no credentials, and does not reference system config paths. All environment/credential access is absent, which is proportionate for a local text-analysis tool.
Persistence & Privilege
Skill is not marked always:true and does not request persistent or elevated privileges. The code operates on inputs it is given and generates reports; it does not modify other skills or global agent configuration.
Assessment
This skill appears to do exactly what it claims: local analysis of article text and AI/originality heuristics. Before installing or running it: 1) ensure you run it in an environment with Python and the requirements (jieba, scikit-learn, numpy) or install them yourself; 2) only pass article texts you are comfortable having processed locally (the tool reads files you give it); 3) if you need network-isolated execution, run it offline — the code shown does not perform network calls, but you should verify the omitted files if you have concerns; 4) expect some heuristics and false positives in AI-detection (the code uses simplified heuristics rather than a full language-model perplexity), so treat results as guidance, not definitive proof of authorship.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.