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The open python-senior-teacher/SKILL.md template is a set of instructions for getting an AI to teach Python through explanations, hints, code review, and debugging questions. You can paste its contents into a web-based LLM’s custom instructions or system prompt, or save it in a project’s skills directory if your agent framework loads skill files. It is a reusable prompt template—not a Python application or a packaged tutoring product.
What the Senior Python Educator template does
Carl Henderson published the template on DEV Community on September 26, 2026. Its aim is to make an AI act less like an answer-search tool and more like a patient programming mentor. Henderson describes the goal as an AI that acts “less like an automated Stack Overflow and more like an authentic, patient Senior Software Engineer and CS Professor.” That is the template’s intended persona, not a claim that an AI becomes a qualified educator.
The instructions bring several kinds of help into one place: explaining concepts, guiding learners through problems, reviewing code, and coaching them through tracebacks. They also include a scenario matrix for different kinds of requests and a glossary using analogies to explain topics such as mutability, dunder methods, iterables and iterators, and decorators.
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How it guides a learning session
Explanations start small
For a concept explanation, the template calls for an analogy, a small Python example, and a short explanation of relevant internals, such as how CPython behaves. The progression is intended to connect an intuitive picture to actual code and then to what happens underneath.
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Hints come before full solutions
When a learner asks how to do something, the template favors a concept blueprint followed by a hint or code skeleton, then a check question. This leaves room for the learner to attempt the work. It also permits a complete solution when the learner explicitly asks for one, so the hint-first approach is a default rather than a refusal to answer.
Reviews use L.I.F.T.
For code review, the template uses four lenses: Logic and Functionality, Idiomatic Python, Formatting and Standards, and Time and Space Complexity. It also asks the AI to identify something the learner did well before suggesting refinements. That makes correctness, readability, conventions, and efficiency separate parts of the review rather than treating every issue as a style problem.
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Tracebacks become diagnostic prompts
For debugging, the instructions ask the AI to point to the relevant line, explain the exception in plain language, and ask a targeted question that helps the learner find the cause. The emphasis is on understanding the failure, not just receiving a corrected snippet.
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The template calls for PEP 8 conventions and type hints, and mentions idioms such as enumerate(), zip(), safe dictionary access, context managers, and generators where appropriate. These are guidance points, not a requirement to force every idiom into every example.
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Choose a setup route
| Route | How to use it | Best fit |
|---|---|---|
| Web LLM custom instructions or system prompt | Copy the prompt content into the custom-instructions field or system prompt available in the interface. | You primarily use a web-based LLM and want the teaching instructions associated with that interface or account. |
| Agent framework skills directory | Save the file as python-senior-teacher/SKILL.md inside the project’s skills/ directory. |
You work in a project-based agent setup that can load a SKILL.md file and want the instructions stored with the project. |
Henderson names Claude Code, OpenClaw, Codex CLI, and Lumo AI in connection with the template. Those are the author’s descriptions of possible use; the article does not establish independent compatibility guarantees for each platform. Check the interface or framework you use to confirm where it accepts custom instructions or whether it loads project skill files.
What the author’s testing does—and does not—show
Henderson says he tested the template notably with Lumo AI and local AI agents and found the results “fantastic.” That is a useful account of his experience, but the article provides no measured learning gains, comparison group, or independent validation. It does not establish that the template improves learning outcomes or works equally well across models and setups.
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The article invokes scaffolding, the Zone of Proximal Development, and active recall as pedagogical ideas behind its approach, but gives no study results showing that this particular template produces those outcomes. Treat them as the rationale for its hint-and-question style, not proof of effectiveness. The article also opens with a £5,000+ boot-camp cost figure without attributing it to a named organization, study, or dataset, so it should not be read as a verified market statistic.
Who may find it useful
- Self-taught Python learners who want an AI to explain concepts and prompt them to reason before giving a solution.
- Learners who want the same instructions to cover debugging and code review as well as new concepts.
- Developers who can adapt a prompt and want to tailor its standards, examples, or troubleshooting style to their needs.
It is less suited to someone who wants only immediate, unscaffolded answers unless they are comfortable explicitly requesting a complete solution. As with any prompt-based approach, the instructions guide the AI’s behavior; they do not guarantee that every response will follow the framework accurately.
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How to adapt the template
Henderson invites readers to consider what is missing, which bad habits or teaching anti-patterns the instructions should discourage, and whether to add modern Python standards, including Python 3.12 or 3.13 features, asyncio rules, or stricter typing guidance. Those are sensible areas to customize if they match the learner’s environment. Keep examples and expectations aligned with the Python version and tools actually used in the project, rather than treating newer features as universally available.
Quick Recap
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