Data Analysis Report
v1.0.0自动分析多种格式数据，生成含图表、关键洞察和建议的中文完整报告，支持本地安全处理和批量文件分析。
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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 promise local data analysis and report generation in Chinese. The included processor (processors/data_processor.py) implements CSV/DataFrame analysis, correlation/outlier detection and Markdown report generation, which aligns with the stated purpose. The SKILL.md lists common Python data libraries (pandas/matplotlib/seaborn/plotly) that are reasonable for this functionality. No unrelated credentials, binaries, or config paths are requested.
Instruction Scope
SKILL.md focuses on local processing and gives triggers and usage guidance. It instructs installing Python dependencies (pip install ...) but provides no runtime steps that read unrelated system files or environment variables. A minor scope note: the doc mentions advanced features like "邮件自动发送报告" (automatic email sending) and "系统集成API" which would require network access and credentials if enabled — however, there are no implementation or credential requests for those features in the provided code.
Install Mechanism
This is an instruction-only skill with one included Python file; there is no formal install spec. SKILL.md recommends 'pip install pandas matplotlib seaborn plotly jupyter openpyxl' — installing packages from PyPI is normal but should be done in a controlled environment (virtualenv/container). No downloads from arbitrary URLs or extract steps were found.
Credentials
The skill requires no environment variables, credentials, or config paths. The code does not read env vars or access external services. The lack of requested secrets is proportionate to the described functionality.
Persistence & Privilege
Flags are default (always: false, user-invocable, model invocation allowed). The skill does not request permanent presence or modify other skills/configs. No privileged persistence behavior was observed.
Assessment
This skill appears internally consistent and implements local data analysis. Before installing: (1) run it inside a virtualenv or isolated environment and review/approve the pip installs; (2) if you plan to enable advanced features like scheduled runs or email sending, expect to supply credentials (SMTP/API keys) and review any added code for network transmission; (3) test on non-sensitive sample data first to confirm no unexpected external calls; (4) if you require a stricter install mechanism, ask the publisher for a formal install spec or packaged release.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.