ai-engineering-from-scratch Skill
rohitg00 による 7 個の Skill(指示セット)。AI が読んでそのとおりに作業します · MCP server ではありません
提供元の説明(英語の原文)
Learn AI Engineering! Learn it. Build it. Ship it for others.
Skill と MCP の違い
MCP は AI を外部のサービスやデータにつなぎます。Skill は AI が読んで従う作業手順書で、何にも接続しません。
使えるアプリ
Claude CodeCodexCursor
提供元の README と repo のフォルダから判断
インストール
Skill は AI が従う指示です。入れる前にファイルを読み、信頼できる提供元のものだけを入れてください。
提供元のおすすめの方法
npx skills add rohitg00/ai-engineering-from-scratch提供元の README から引用。提供元のコードをダウンロードして実行します
どのアプリを使っていますか?
ターミナルを開く
Mac:Command+スペースで Terminal と入力 · Windows:PowerShell を開く(下のコマンドは Mac/Linux 用)
skill フォルダを ~/.claude/skills にコピー
repo を一時フォルダにダウンロードし、SKILL.md のあるフォルダだけをコピーします
rm -rf /tmp/popnx-skill && git clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratch.git /tmp/popnx-skill && mkdir -p ~/.claude/skills && cp -R '/tmp/popnx-skill/skills/.' ~/.claude/skills && rm -rf /tmp/popnx-skillClaude Code を開き直し、/learn と入力するか関連する作業を頼むと Claude が自動で使います
ターミナルを開く
Mac:Command+スペースで Terminal と入力 · Windows:PowerShell を開く(下のコマンドは Mac/Linux 用)
skill フォルダを ~/.agents/skills にコピー
repo を一時フォルダにダウンロードし、SKILL.md のあるフォルダだけをコピーします
rm -rf /tmp/popnx-skill && git clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratch.git /tmp/popnx-skill && mkdir -p ~/.agents/skills && cp -R '/tmp/popnx-skill/skills/.' ~/.agents/skills && rm -rf /tmp/popnx-skillCodex を開き直して関連する作業を頼むと、Codex が skill を一覧に出します
ターミナルを開く
Mac:Command+スペースで Terminal と入力 · Windows:PowerShell を開く(下のコマンドは Mac/Linux 用)
skill フォルダを ~/.cursor/skills にコピー
repo を一時フォルダにダウンロードし、SKILL.md のあるフォルダだけをコピーします
rm -rf /tmp/popnx-skill && git clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratch.git /tmp/popnx-skill && mkdir -p ~/.cursor/skills && cp -R '/tmp/popnx-skill/skills/.' ~/.cursor/skills && rm -rf /tmp/popnx-skillCursor の Agent チャットで / を入力し learn を選ぶ
Gemini CLI や GitHub Copilot など Agent Skills 対応の他のアプリはフォルダが違います。そのアプリのガイドを見て、SKILL.md のあるフォルダをコピーしてください。
2026年11月11日 に Claude Code、Codex、Cursor の公式ガイドで skill フォルダを確認
インストール前に SKILL.md を読む
以下は提供元の repo の文章で、PopNx MCP の指示ではありません
---
name: learn
version: 1.0.0
description: >
Interactive lesson tutor for the AI Engineering from Scratch curriculum.
Reads LEARNING.md, fetches the next lesson, teaches it section by section
in the terminal, quizzes at the end, and records progress. Works cloned or
entirely over raw.githubusercontent.com — no setup required.
Trigger phrases: "next lesson", "teach me", "continue the course",
"let's learn", "resume learning"
tags: [tutor, curriculum, ai-engineering, interactive-learning]
---
# Learn
You are the tutor for the **AI Engineering from Scratch** curriculum. One
invocation = one lesson, taught interactively: the learner should type,
answer, and run things — never just scroll. Works with any agent.
## Host invocation contract
Skill names are portable, but invocation syntax belongs to the host. Render
every suggested next action in the correct form:
- Codex: `learn`, `start-learning`, `check-understanding 13`, and other
`skill-name` forms, or tell the learner to choose the skill from `/skills`.
- Claude Code: `/learn`, `/start-learning`, `/check-understanding 13`, and
other `/skill-name` forms.
- Other compatible hosts: natural language such as `Use start-learning to
build my course plan.` or `Use check-understanding to quiz me on Phase 13.`
Never present a slash command as universal syntax. If the host is unknown,
use the natural-language form.
## Content sources
Prefer local files when the repo is cloned (a `phases/` directory exists in
or above the current directory). Otherwise fetch from:
```text
https://raw.githubusercontent.com/rohitg00/ai-engineering-from-scratch/main/<path>
```
- Lesson text: `phases/<phase-dir>/<lesson-dir>/docs/en.md`
- Lesson quiz: `phases/<phase-dir>/<lesson-dir>/quiz.json`
- Lesson list for a phase: the Contents section of `README.md` (each phase's
table lists every lesson with its directory path and title)
## Resume routing across course modes
Before Step 0, resolve every "resume" or "continue" request against these
supported state files and their route owners:
- `LEARNING.md` belongs to `learn` for the full curriculum.
- `MCP-LEARNING.md` belongs to `learn-mcp` for the Model Context Protocol
(MCP) route.
- `MCP-ENGINEERING-LEARNING.md` is the legacy filename for that same
`learn-mcp` route, not a separate route.
- `AGENT-SKILLS-LEARNING.md` belongs to `learn-agent-skills`.
- `REVERSE-ENGINEERING-LEARNING.md` belongs to `learn-reverse-engineering`.
- `CLAUDE-CERTIFICATION.md` belongs to `claude-certification`.
- `INTERVIEW-LEARNING.md` belongs to `interview-prep` for interview practice.
If the learner names a route in a resume or continue request, dispatch to its
owner immediately even when other state files exist. If that owner is `learn`,
continue to Step 0; otherwise invoke the named owner and stop this skill.
For an unnamed resume or continue request, collect the owners whose state files
exist, grouping both MCP filenames under `learn-mcp`. If exactly one route owner
remains, resume it before Step 0: continue here only for `learn`; otherwise
invoke that owner and stop this skill. `learn-mcp` owns legacy-file migration
and collision reporting. If two or more route owners remain, list their
learner-facing route names and ask which route to resume before selecting a
lesson or changing any state. If none exist, continue to Step 0. Never infer a
route from file recency or merge one route's progress into another state file.
Legacy runtimes may expose `learn-mcp-engineering` as an alias. Accept it only
to reach `learn-mcp`; render every learner-facing handoff as `learn-mcp` and
name the route Model Context Protocol (MCP).
## Focused MCP handoff
If the learner asks for the Model Context Protocol (MCP) path, or either
`MCP-LEARNING.md` or `MCP-ENGINEERING-LEARNING.md` exists and they ask to
resume MCP, hand off to the portable skill `learn-mcp`. The focused tutor
migrates the legacy filename without discarding learner evidence. Its source
of truth is `learning-paths/model-context-protocol.json`. Do not choose the
next numeric Phase 13
lesson and do not copy MCP state into `LEARNING.md`; the dedicated tutor owns
route order, wire checkpoints, and the security gate.
## Focused Agent Skills handoff
If the learner asks for the Agent Skills route, or
`AGENT-SKILLS-LEARNING.md` exists and they ask to continue or resume Agent
Skills, hand off to the portable skill `learn-agent-skills`. Its source of
truth is `learning-paths/agent-skills.json`. Render the handoff with the host
invocation contract. Do not choose the next numeric Phase 13 lesson and do not
copy Agent Skills state into `LEARNING.md`; the dedicated tutor owns the
five-lesson order, real-host evidence, sandbox boundaries, the Lesson 25 and
tool-poisoning prerequisite gate before Lesson 26, and the release gate.
## Focused Reverse Engineering handoff
If the learner asks for the Reverse Engineering route, or
`REVERSE-ENGINEERING-LEARNING.md` exists and they ask to continue or resume it,
hand off to the portable skill `learn-reverse-engineering`. Its source of truth
is `learning-paths/reverse-engineering.json`. Render the handoff with the host
invocation contract. Do not copy its state into `LEARNING.md`; the dedicated
tutor owns the fourteen-lesson order and the `own-binaries-only` check before
Lesson 32.
## Lesson screen
The portable skill `lesson-screen` opens a browser screen next to the
terminal. It shows the lesson's registered figures, code traces with real
values, and clickable predictions. It also builds a figure for the lesson on
the fly: a blueprint, a mechanism, an isometric machine, or a 3D model.
- Offer it once per session, before Step 2, when the host can run `node` 20
or later and open a browser on the learner's machine. Render the skill name
with the host invocation contract. If the learner accepts, follow
`lesson-screen` and keep teaching here.
- Right after the learner accepts, offer one figure built for this lesson,
with its time from `lesson-screen`. Build it while you teach, so that it is
ready at See it.
- If the learner declines, or no local browser is available, give the lesson
page once:
`https://aiengineeringfromscratch.com/lesson?path=<phase-dir>/<lesson-dir>`.
It shows the same registered figures.
- The terminal stays the place where the learner answers. Read screen clicks
with its `events` command before each reply. Screen clicks are hints for
you. They never count as quiz answers, checkpoint evidence, or lab results.
- Never block or slow the lesson on the screen. If the server fails, say so
in one line and continue in the terminal.
## Step 0 — Locate state
Read `LEARNING.md` from the current directory.
- **Found**: the next lesson is the first not-yet-logged lesson of the first
phase whose Status is `Do` or `Review` (phase order, lesson order). If the
learner names a lesson or topic explicitly ("teach me backprop"), honor
that instead and note the detour in the log.
- **Found, but no eligible lesson remains** (every `Do`/`Review` phase is
fully logged): do not teach. Congratulate them on completing their path,
set any finished phases' Status to `Done`, and offer three real options:
work the Review queue, use `check-understanding` on a phase of their choice,
or use `start-learning` to extend the plan into skipped phases. Render both
skill calls with the host invocation contract.
- **Missing**: say that `start-learning` builds a personalized plan, render it
with the host invocation contract, and
offer two options — run it now, or start immediately at Phase 1, Lesson 1
without a plan. Never block the lesson on setup.
## Step 1 — Warm-up recall (only if a previous lesson is logged)
Before new material, ask 2 questions from the **previous** lesson's quiz,
picked at random. No stakes, no score — one sentence of feedback per answer.
Retrieval after a gap is what moves knowledge to long-term memory; that is
this step's entire job. If the learn話し合い
質問、コツの共有、使えないことの報告ができます。「役に立った」の多い順です。
GitHub の情報
- GitHub スター
- 78,414
- 提供元
- rohitg00
- ライセンス
- MIT
- 最終コード更新
- 2026年11月11日
- repo 内の SKILL.md
- 13
- トピック
- agents, ai, ai-agents, ai-engineering, computer-vision, course, deep-learning, dsh, from-scratch, generative-ai, llm, machine-learning