ee-ai-toolkit
v1.0.0电气工程师 AI 工具包。用于 AI in Electrical Engineering、电气工程 AI、prompt engineering、power systems、smart grids、electrical calculations、design automation、data visualizatio...
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byJohn Do@junwugit
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
Capability signals
These labels describe what authority the skill may exercise. They are separate from suspicious or malicious moderation verdicts.
OpenClaw
Benign
high confidencePurpose & Capability
The name/description (EE AI toolkit, 100 Python examples, prompt library, course materials) align with the included files (references/*.md, assets/python-scripts/*) and the single required binary (python3). The requested capabilities are proportional to its stated purpose.
Instruction Scope
SKILL.md instructs the agent to read local course files and to prefer using the included Python example scripts; it provides a small local search utility for locating material. There are no instructions to read unrelated system files, fetch secrets, or transmit data externally. It does note the option to extract assets/source-html.tar.gz if HTML verification is needed.
Install Mechanism
No install spec is present (instruction-only with bundled files), so nothing is downloaded or executed automatically during install. This is the lowest‑risk install model for a skill.
Credentials
The skill declares no required environment variables or credentials. The optional Python libraries (numpy, pandas, matplotlib, scikit‑learn) mentioned in SKILL.md are appropriate for the examples and are not requested as secrets or system credentials.
Persistence & Privilege
always is false and autonomous invocation is enabled (the platform default). The skill does not request persistent elevated privileges or to modify other skills' configs.
Assessment
This package appears to be an offline course/toolkit that reads and runs local Python examples. Before installing or running code: (1) review any scripts you plan to run (especially those you haven't inspected) for network calls or filesystem writes; (2) run code in a sandbox or isolated environment if you are concerned; (3) ensure required Python libraries (numpy/pandas/scikit‑learn/matplotlib) are installed in a controlled environment; (4) be cautious when extracting assets/source-html.tar.gz—verify its contents first. If you want, I can scan the remaining omitted scripts for network/file I/O patterns or highlight any files that write to disk or call external services.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
Binspython3