Financial Analytics Pro 安全扫描报告 — 可信 | ClawSafe

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Financial Analytics Pro

Premium financial analysis tool for business intelligence, financial statement analysis, ratio calculations, forecasting, and visualization

This is a legitimate financial analysis tool that performs standard CSV/Excel data analysis, ratio calculations, forecasting, and visualization with no malicious behavior detected.

技能名称Financial Analytics Pro

分析耗时35.1s

引擎pi

✓

可以安装

No action required. The skill is safe to use as it contains only standard financial analysis code with no security risks.

安全发现 1 项

严重性	安全发现	位置
低危	Documentation exceeds implementation 文档欺骗 SKILL.md describes banking API integrations (Plaid, Yodlee), e-commerce platform connectors, and accounting software that are not implemented in the actual code. This is likely incomplete development rather than malicious intent. `Bank API Connections: Connect to Plaid, Yodlee, or direct bank APIs` → Complete the promised integrations or remove them from documentation	`SKILL.md:35`

资源类型	声明权限	推断权限	状态	证据
文件系统	`READ`	`READ`	✓ 一致	Uses pandas to read CSV/Excel files
网络访问	`NONE`	`NONE`	—	No network requests in code
命令执行	`NONE`	`NONE`	—	No subprocess or shell execution
环境变量	`NONE`	`NONE`	—	No os.environ access for credential harvesting
数据库	`NONE`	`NONE`	—	No database access

目录结构

4 文件 · 27.7 KB · 785 行

Markdown 2f · 487L Python 1f · 286L CSV 1f · 12L

├─ ▾ 📁 examples

│ └─ 📄 sample_financials.csv CSV 12L · 836 B

├─ ▾ 📁 references

│ └─ 📝 financial_ratios_cheat_sheet.md Markdown 221L · 5.1 KB

├─ ▾ 📁 scripts

│ └─ 🐍 financial_analyzer.py Python 286L · 11.2 KB

└─ 📝 SKILL.md Markdown 266L · 10.5 KB

依赖分析 4 项

包名	版本	来源	已知漏洞	备注
`pandas`	`*`	pip	否	Standard data analysis library
`numpy`	`*`	pip	否	Numerical computing
`matplotlib`	`*`	pip	否	Visualization library
`seaborn`	`*`	pip	否	Statistical visualization

安全亮点

✓ No credential harvesting or environment variable access

✓ No network requests or data exfiltration

✓ No shell execution or subprocess calls

✓ No obfuscated code or base64-encoded commands

✓ No access to sensitive paths (~/.ssh, ~/.aws, .env)

✓ No reverse shell or C2 communication patterns

✓ Standard financial calculations using well-known libraries (pandas, numpy)

✓ File operations limited to reading CSV/Excel and saving to user-specified locations

✓ No malicious dependencies or supply chain risks