Financial Analyzer
v1.0.0AI-powered financial analysis assistant for financial statement analysis, ratio analysis, cash flow analysis, investment evaluation, and financial health ass...
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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
The name/description (financial analysis) matches the included Python implementation and SKILL.md examples. The skill requests no credentials or system access. One minor concern: the skill's source/homepage are unknown which reduces provenance/accountability, but functionally the requirements are proportionate to its stated purpose.
Instruction Scope
SKILL.md instructs local usage and shows example APIs that take user-provided financial statements. It does not direct reading unrelated system files, accessing external endpoints, or exfiltrating data. The runtime instructions are narrowly scoped to analysis tasks.
Install Mechanism
There is no formal install spec in the package metadata; SKILL.md recommends 'pip install numpy pandas', which is appropriate and proportionate for numeric processing. No downloads from arbitrary URLs, no extract/install hooks, and the code file contains only local computations.
Credentials
No required environment variables, no primary credential, and no config paths are requested. That is consistent with a local analysis library that operates on user-supplied data.
Persistence & Privilege
The skill is not marked 'always: true' and uses only in-memory history storage. It does not modify other skills or system-wide configuration. Autonomous invocation is allowed by default but that is normal and not an additional red flag here.
Assessment
This skill appears to perform local financial calculations only, which is coherent with its description. Before installing: 1) Note the package author/source/homepage is missing — prefer code from a known repository or author if you require provenance. 2) The SKILL.md recommends installing numpy and pandas via pip; install those packages from the official PyPI index and consider using a virtual environment. 3) Review any company financial data you feed into the skill for sensitivity (it does not send data outbound, but you should avoid sharing confidential data with third-party code unless you trust its source). 4) If you plan to rely on its outputs for real investment decisions, validate results on known examples and consider an independent audit of the calculations.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.