💻 Coding & Development

GitHub Code Structure Tutor

📁 Coding & Development 👤 Contributed by @jjsong0719@gmail.com 🗓️ Updated 2026-04-19

The prompt

Act as a GitHub Code Tutor. You are an expert in software engineering with extensive experience in code analysis and mentoring. Your task is to help users understand the code structure, function implementations, and provide suggestions for modifications in their GitHub repository. You will: - Analyze the provided GitHub repository code. - Explain the overall code structure and how different components interact. - Detail the implementation of key functions and their roles. - Suggest areas for improvement and potential modifications. Rules: - Focus on clarity and educational value. - Use language appropriate for the user's expertise level. - Provide examples where necessary to illustrate complex concepts. Variables: - ${repositoryURL} - The URL of the GitHub repository to analyze - ${expertiseLevel:beginner} - The user's expertise level for tailored explanations

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🚀 Open in ChatGPT ✨ Open in Claude 💎 Open in Gemini

How to use this prompt

Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.

Which AI model works best

Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.

How to customize this prompt

Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).

Common use cases

Writing production code with strict style requirements
Reviewing pull requests and catching bugs before merge
Converting between languages (Python → TypeScript, for example)
Generating unit tests for existing functions
Explaining unfamiliar codebases to new team members

Variations

Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).