ee-ai-toolkit

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...

Security Scan

The skill's files, runtime instructions, and requirements are consistent with an educational electrical‑engineering AI toolkit that expects python3 and local course/script assets; nothing in the manifest or SKILL.md requests unrelated credentials, installs, or external endpoints.

Purpose & 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.

这个技能由本目录的 HTML 课程资料压缩生成，主题是电气工程师如何用 AI、Python 和提示工程处理计算、设计、分析、自动化、优化和职业发展任务。

激活此技能时，优先按问题类型读取最小必要资料：

• 课程结构、资料来源、主题路由：读取 references/course-index.md。
• 快速回答或复习：读取 references/condensed-lessons.md。
• 需要接近原文、练习、示例流程或完整上下文：读取 references/source-digest.md。
• 提示词、提示词改写、提示词模板：读取 references/prompt-library.md。
• Python 示例脚本、脚本编号、脚本用途：读取 references/python-script-catalog.md，再使用 assets/python-scripts/ 中的对应脚本。
• 需要核对原始 HTML：使用 assets/source-html/，或解压 assets/source-html.tar.gz。

资料较大时，先用检索脚本定位，再读取相关引用文件：

python3 {baseDir}/scripts/search_ee_ai.py --query "load forecasting"

生成或修改工程答案时，保持以下约束：

• 明确单位、输入假设、公式、计算步骤和验证方法。
• AI 生成的设计、保护、配电、并网、优化和故障分析结果只能作为工程草案或教学示例。
• 对安全关键或合规相关电气工程问题，要求用户用适用标准、仿真工具、现场数据和有资质工程审查进行验证。
• 需要代码时，优先复用或改造 assets/python-scripts/ 中最接近的脚本，而不是从零编造。
• 回答可以中英文混合，但用户用中文提问时默认用中文回答。