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

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Última actividad de origen registrada: 6 de septiembre de 2026 a las 03:56

Catálogo de SkillsMP actualizado: 26 de octubre de 2026 a las 11:37

skills recopiladas: 4

Estrellas en GitHub: 802
Forks en GitHub: 108

README del repositorio

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# 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: - **If you're solving things easily:** AI asks deeper "why" questions, introduces harder constraints, and reduces unnecessary prompting. - **If you're struggling:** AI lowers the hint level, revisits prerequisites, and creates targeted practice — without just giving you the answer. --- ## 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 - **GitHub Copilot** (Chat mode) in your editor - No other dependencies, scripts, or setup needed --- ## Limitations - **Skills** require a Copilot plan that supports custom skills. Prompts work everywhere. - **Learning logs** are optional and manual. The toolkit works fine without them. - **Inline completions** must be disabled manually in your editor settings. - This is a prompt-based toolkit, not an app. It works through Copilot Chat conversations. --- ## 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.

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Skills en este repositorio

Profesores postsecundarios, todos los demás Analistas de garantía de calidad de software y probadores Desarrolladores de software

3 categorías ocupacionales · 100% clasificado

skill

ocupación

descripción

actualizado

code-review

Analistas de garantía de calidad de software y probadores

Review learner code for risk while teaching the reasoning behind findings.

Idioma del texto original: inglés

2026-09-06

debugging

Desarrolladores de software

Diagnose failures by testing learner hypotheses with observable evidence.

Idioma del texto original: inglés

2026-09-06

examination

Profesores postsecundarios, todos los demás

Test understanding through prediction, explanation, application, and transfer.

Idioma del texto original: inglés

2026-09-06

retrieval

Profesores postsecundarios, todos los demás

Revisit learning-log material with short, varied recall practice.

Idioma del texto original: inglés

2026-09-06

Mostrando 4 de 4 skills recopiladas.

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Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/i-am-manware/Manware-s-AI-Learning-Toolkit.