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ai-engineering-from-scratch Skill

de rohitg00★ 78.4147 skills

Instalar

7 skills (conjuntos de instrucciones) de rohitg00 que la IA lee y sigue · no es un MCP server

Descripción del autor (inglés original)

Learn AI Engineering! Learn it. Build it. Ship it for others.

En qué se diferencia un Skill de MCP

MCP conecta la IA con un servicio o datos externos. Un Skill es una guía de trabajo que la IA lee y sigue; no se conecta a nada.

Funciona con

Claude CodeCodexCursor

Según el README del autor y las carpetas del repo

Instalar

Un Skill son instrucciones que la IA seguirá. Lee los archivos antes de instalar e instala solo de autores de confianza.

La forma que recomienda el autor

npx skills add rohitg00/ai-engineering-from-scratch

Copiado del README del autor; descarga y ejecuta su código

¿Qué app usas?

  1. Abre la Terminal

    Mac: pulsa Command+Espacio y escribe Terminal · Windows: abre PowerShell (el comando es para Mac/Linux)

  2. Copia la carpeta del skill a ~/.claude/skills

    Descarga el repo a una carpeta temporal y copia solo las carpetas con SKILL.md

    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-skill
  3. Vuelve a abrir Claude Code y escribe /learn, o pide una tarea relacionada y Claude lo usará

  1. Abre la Terminal

    Mac: pulsa Command+Espacio y escribe Terminal · Windows: abre PowerShell (el comando es para Mac/Linux)

  2. Copia la carpeta del skill a ~/.agents/skills

    Descarga el repo a una carpeta temporal y copia solo las carpetas con SKILL.md

    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-skill
  3. Vuelve a abrir Codex y pide una tarea relacionada; Codex mostrará el skill

  1. Abre la Terminal

    Mac: pulsa Command+Espacio y escribe Terminal · Windows: abre PowerShell (el comando es para Mac/Linux)

  2. Copia la carpeta del skill a ~/.cursor/skills

    Descarga el repo a una carpeta temporal y copia solo las carpetas con SKILL.md

    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-skill
  3. En el chat Agent de Cursor escribe / y elige learn

Otras apps con Agent Skills (como Gemini CLI o GitHub Copilot) usan otras carpetas. Mira la guía de esa app y copia allí la carpeta con el SKILL.md.

Ver en GitHub ↗

Carpetas de skills comprobadas con las guías oficiales de Claude Code, Codex y Cursor el 11 nov 2026

Lee el SKILL.md antes de instalar

El texto siguiente viene del repo del autor, no de PopNx MCP

---
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

Conversación

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Desde GitHub

Estrellas en GitHub
78.414
Autor
rohitg00
Último cambio de código
11 nov 2026
Archivos SKILL.md del repo
13
Temas
agents, ai, ai-agents, ai-engineering, computer-vision, course, deep-learning, dsh, from-scratch, generative-ai, llm, machine-learning