Manware-s-AI-Learning-Toolkit README

The project's README from github.com/i-am-manware/Manware-s-AI-Learning-Toolkit. As one markdown file: README.md.

Manware's AI Learning Toolkit

A customization that turns AI into a learning companion instead of a code generator. It makes you think, predict, and reason — while AI guides, questions, and diagnoses.

Core idea: You try first. AI helps only after you commit to an answer.

Looking for a different AI agent? This repo has separate branches for each supported tool. Switch to the branch matching your agent — copilot, cursor, claude-code, opencode, or antigravity — to get the right configuration and code.

How It Works

You try → You predict → AI gives hints → You implement → You test → You explain → AI reviews → You retrieve later

AI never writes the solution for you. It asks questions, gives small hints, and tests your understanding. The harder you think, the more you learn.

Quick Start

  1. Clone or copy this repo into your project (or use it as a template).
  2. Open GitHub Copilot Chat in your editor.
  3. Type a slash command like /hint, /debug, /explain, etc.
  4. Answer the questions Copilot asks you. Don't skip them — that's where the learning happens.
  5. Write the code yourself. AI will guide, not write.

Important!: Disable Copilot inline completions while learning. Use Chat mode only.

The 12 Prompts

Each prompt is a workflow. Invoke it with a /command in Copilot Chat.

/learn

The entry point. Tell it what you want to learn, and it picks the right workflow for you. Use this when you're not sure which prompt to use.

Use when: You're starting a session and don't know where to begin.

/hint

Your main problem-solving tool. First, you predict what should happen. Then, AI gives you the smallest hint needed — one level at a time, from a question all the way up to full code (only if you ask for it).

Use when: You're stuck on a problem, bug, or concept and want incremental help without being handed the answer.

/debug

Bug diagnosis through hypothesis testing. AI asks what you expected, what happened, and what you think is wrong — then guides you to find the bug yourself.

Use when: Your code doesn't work and you want to understand why, not just get a fix.

/autopsy

Post-mortem after fixing a bug. You fill in what happened, what you believed, what actually happened, and what you missed. AI classifies the bug and suggests prevention.

Use when: You just fixed a non-trivial bug and want to learn from it so it doesn't happen again.

/read

Understand unfamiliar code. AI asks you concrete questions one at a time: "What does this input produce?" "What happens after iteration 3?" You reconstruct the mental model yourself.

Use when: You're reading someone else's code, old code, framework internals, or library source.

/code-review

Educational code review. Before AI reviews your code, you identify the weakest part yourself. Then AI labels issues by severity and asks discovery questions instead of rewriting your code.

Use when: You've written something and want a senior-developer-style review that teaches you.

/test

Design tests before implementing. You state the contract, identify the smallest passing/failing inputs, and derive test cases. AI exposes ambiguities in your spec.

Use when: You're about to implement a feature and want to think through edge cases first.

/explore

Explore design space. You defend your current solution, AI offers conceptually different alternatives, then introduces constraints one at a time to stress-test your design.

Use when: You have a working solution and want to understand tradeoffs, or when you want to practice handling real-world constraints like scale, concurrency, or failure.

/arch

Architecture interview. AI asks one focused question at a time about requirements, state, interfaces, failure modes, etc. You sketch the design; AI challenges your assumptions.

Use when: You're designing a non-trivial feature and want to think through it before coding.

/explain

Teach-back test. You explain a concept in your own words, as if teaching a beginner. AI probes for gaps, vague terms, and contradictions — then gives a concise assessment.

Use when: You think you understand something and want to verify it.

/retrieve

Spaced retrieval practice. AI reads your learning logs (if they exist) and asks you a short mix of prediction, debugging, and application questions from past material.

Use when: You're starting a coding session and want a quick warm-up to reinforce what you've learned.

/api

Learn an API deeply. AI walks you through 7 questions: what problem it solves, what assumptions it makes, what alternatives exist, when not to use it, and its failure modes. Then it points you to official docs.

Use when: You encounter a new library, framework, or API and want real understanding, not just syntax.

The 4 Skills

Skills are reusable behaviors that work across multiple prompts. You don't invoke them directly, they shape how AI responds when relevant.

Skill What it does
debugging Separates expected vs actual behavior, requires a hypothesis before suggesting causes, picks the smallest next experiment.
examination Tests understanding through prediction, explanation, application, and transfer. Corrects the smallest misconception first.
code-review Prioritizes correctness over style, asks discovery questions before rewrites, distinguishes bugs from preferences.
retrieval Mixes recent and older material, prefers prediction over definitions, adapts difficulty to your performance.

Learning Logs (Optional)

A lightweight folder for recording meaningful learning events. You don't need to update these after every interaction, but only when something sticks, or at the end of the day.

learning/
├── mistakes.md    # Bugs, misconceptions, recurring patterns
├── concepts.md    # Durable understanding worth keeping
├── questions.md   # Unresolved questions to revisit
└── review.md      # Retrieval prompts and review metadata

The /retrieve prompt reads these files to personalize your review sessions.

Adaptive Behavior

The toolkit adapts to you:

Shipping Mode

This toolkit is for learning. When you need to ship:

Just say "ship this" or ask for a direct implementation.

AI will switch to normal engineering mode. The learning rules only apply when you invoke a learning prompt.

How It's Organized

.github/
├── copilot-instructions.md    # Global learning philosophy
├── prompts/                   # 12 user-facing workflows (slash commands)
├── instructions/              # Rules for learning log files
└── skills/                    # 4 reusable AI behaviors
learning/                      # Optional learning logs

Requirements

Limitations

Philosophy

Make AI reduce the friction around learning programming without reducing the amount of thinking the learner has to do.

If you're not thinking, you're not learning.

Manware-s-AI-Learning-Toolkit (i-am-manware/Manware-s-AI-Learning-Toolkit)

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799 ·GitHub
Manware-s-AI-Learning-Toolkit
Что это?Это настройка, превращающая ИИ в учебного напарника, а не генератор кода: сначала пробуете и предсказываете вы, а ИИ помогает только после того, как вы дали свой ответ, — направляя вопросами и подсказками, а не записывая решение.
Это приложение или просто набор промптов для моего редактора?Это не приложение, а набор инструментов на промптах: промпты-слэш-команды и скиллы, которые вы копируете в свой проект и вызываете внутри GitHub Copilot Chat (или аналогичного чат-режима в Cursor, Claude Code, opencode или Antigravity).
Как начать им пользоваться?Склонируйте или скопируйте репозиторий в свой проект (или используйте как шаблон), откройте Copilot Chat в редакторе, введите слэш-команду вроде /hint, /debug или /explain, а затем отвечайте на её вопросы, а не пропускайте их.
С какими кодинг-агентами это работает и нужен ли особый тариф Copilot?Набор создан в первую очередь для режима GitHub Copilot Chat, с отдельными ветками для Cursor, Claude Code, opencode и Antigravity; других зависимостей, скриптов или настройки не требуется.
Есть ли ограничения, о которых стоит знать заранее?Переиспользуемым скиллам (debugging, examination, code-review, retrieval) нужен тариф Copilot с поддержкой кастомных скиллов, хотя сами промпты-слэш-команды работают везде; встроенные автодополнения кода придётся отключать вручную, а необязательные журналы обучения ведутся вручную, без автоматического отслеживания.
Для чего нужны 12 промптов?Они покрывают весь учебный процесс: /learn подбирает подходящий промпт, /hint даёт подсказки по нарастающей, /debug и /autopsy разбирают баги, /read и /explain строят понимание, /code-review и /test воспитывают дисциплину, /explore и /arch тестируют дизайн на прочность, а /retrieve проводит интервальное повторение по вашим журналам.
А если мне сейчас нужен просто рабочий код, а не урок?Скажите 'ship this' или попросите прямую реализацию — ИИ переключится в обычный инженерный режим, отбросив учебные правила.
Что за 4 скилла и чем они отличаются от промптов?Скиллы (debugging, examination, code-review, retrieval) — это модели поведения, которые автоматически задают реакцию ИИ во многих промптах, а не команды, которые вы вызываете напрямую.
Подстраивается ли набор под мой уровень?Да — если у вас всё получается легко, он задаёт более глубокие вопросы «почему» и добавляет условия посложнее, а если вы буксуете — снижает уровень подсказок и возвращается к базе, вместо того чтобы просто дать ответ.
What are similar projects?superpowers-zh, pstack, anti-slop, compound-engineering-plugin, hello-sdd, no-mistakes, superpowers, HERO-Anti-OverDefense
Which topics is it in?developer, prompt, antigravity, vscode-extension, github-copilot, code-review, debugging, opencode, prompt-engineering, skill, cursor, learning-resources
Creator
i-am-manware
Size
0.1 MB
Stars
799
Updated
CommitSep 06
Age
1mo
Commits · 90 Days