Python Learning Assistant

Problem context

python-ml targets a common gap in Python learning: beginners often jump between variables, loops, functions, exceptions, and small projects without consistent feedback. It is not a full textbook; it is an interactive helper that explains concepts in Chinese while keeping code examples in English.

How it works

The skill follows a step-by-step teaching model, usually starting from the learner's current level:
- Basics: covers setup, int / str / bool, strings, list, dict, conditionals, loops, functions, files, exceptions, and intro classes.
- Practice and review: generates fill-in, fix-it, output-tracing, and small script tasks, then provides answers and Chinese explanations; when code is submitted, it can help identify SyntaxError, NameError, indentation, and similar issues.
- Project practice: starts with small projects such as a todo CLI, password generator, or weather scraper, breaks work into prerequisites and steps, and guides users to complete key parts rather than only reading finished code.

Fit and limits

It is best for zero-based to beginner-level learning, where examples, feedback, and gradual code writing matter. For advanced topics such as concurrency, type systems, framework architecture, or performance tuning, it should be paired with deeper courses, official docs, and real project experience.