I turned my AI into a Senior Python Educator for self-taught devs, Here’s the open SKILL.md template
Boot camps cost £5,000+. College CS degrees cost even more. For many self-taught developers, relying on free online resources and AI tools is the only realistic path into software engineering. However, default AI models make terrible teachers . When you ask standard ChatGPT, Claude, or Lumo "How do I do X in Python?", they hand you a complete, copy-pasteable solution instantly. It feels…
A self-taught developer seeks to create an AI that functions as a senior Python educator and mentor for novice developers. The goal is to provide more effective teaching than default AI models, which often give complete, copy-paste solutions that bypass the crucial learning phases of struggle, active recall, and problem-solving.
The developer wants an AI that acts like a genuine senior software engineer and CS professor, scaffolding hints, translating complex jargon, conducting code reviews, and guiding students to debug their own errors.
The source material outlines the SKILL.md template for the custom skill, which can be saved in a "python-senior-teacher/" directory. It uses the standard Agent Skills schema, compatible with Claude Code, OpenClaw, Codex CLI, Lumo AI custom skills, or standard system prompts/custom instructions. The skill aims to teach Python concepts, review code, debug exceptions, practice coding exercises, and explain computer science fundamentals in Python.
The template contains a comprehensive pedagogical framework, grounded in computer science education concepts like Scaffolding & Zone of Proximal Development (ZPD), Cognitive Translation (Analogy → Syntax → Mechanism), and the L.I.F.T. Code Review Framework. The 3-Tier Conceptual Breakdown introduces a new Python topic, syntax element, or term through a mental model/analogy, explicit Python example, and under-the-hood context.
When asked "How do I do X?", the AI follows a Socratic Scaffolding approach, providing a 3-step response: Concept Blueprint, Guided Hint/Skeleton, and Check Question.
The Code Review Protocol evaluates submitted code using the L.I.F.T. Framework, focusing on logic & functionality, idiomatic Python, formatting & standards, and time/space complexity. Error Diagnostic Coaching guides students through error traces, translating exceptions into plain English and asking targeted questions to help them identify and fix issues. Technical Domain Standards require all code snippets to adhere to PEP 8 guidelines, type hinting, Pythonic idioms, and other best practices.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.