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Agent skill

AI Engineering Course Tutor

by rohitg00 in rohitg00/ai-engineering-from-scratch

Teaches the next lesson of the AI Engineering from Scratch curriculum in the terminal, quizzes you at the end and records your progress.

MITAuto-check passedEducation

Install AI Engineering Course Tutor

skills CLI
$ npx skills add rohitg00/ai-engineering-from-scratch --skill learn -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install rohitg00/ai-engineering-from-scratch learn --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratch.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/learn .claude/skills/learn && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
learn
GitHub stars
66k
Token cost
~2.2k tokens
SKILL.md length
1,237 words
Files
1
Skills in repo
16
Repo updated
First seen
Licence
MIT

At a glance

Teaches the next lesson of the AI Engineering from Scratch curriculum in the terminal, quizzes you at the end and records your progress.

  • Works in 6 steps: Locate state → Warm-up recall (only if a previous… → Teach the lesson → …
  • Continuing a self-paced AI engineering course in the terminal
  • SKILL.md covers Host invocation contract, Content sources, Resume routing across course… and Focused MCP handoff, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

One invocation teaches one lesson interactively: the learner is expected to type, answer and run things rather than just scroll. The tutor reads the learner's progress file, `LEARNING.md`, fetches the next lesson from a cloned repository or directly from raw.githubusercontent.com with no setup, teaches it section by section, runs the lesson quiz and records progress.

Resume requests are routed by state file: `LEARNING.md` belongs to this skill, while other state files belong to the separate MCP, agent skills and Claude certification courses, which have their own skills. The skill also adapts invocation syntax to the host, using plain skill names in Codex, slash commands in Claude Code and natural-language requests elsewhere, and never presents a slash command as universal. Lesson text and quizzes live under `phases/` folders in the repository.

When your agent uses it

  • Continuing a self-paced AI engineering course in the terminal
  • Taking a lesson quiz and recording progress
  • Resuming a course when several course state files exist

Example prompts

  • “Teach me the next lesson.”
  • “Continue the course where I left off.”
  • “Resume learning and quiz me at the end of the lesson.”

Requirements

  • Network access to raw.githubusercontent.com, unless the course repository is cloned locally

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Locate state
  2. Warm-up recall (only if a previous lesson is logged)
  3. Teach the lesson
  4. Quiz
  5. Record
  6. Close

What it can do on your machine

Read from SKILL.md and the folder at commit cdfd9df. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AI Engineering Course Tutor loads about 2.2k tokens when it runs. Until then it costs about 97 tokens; SKILL.md has 1,237 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~97
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from rohitg00/ai-engineering-from-scratch at commit cdfd9df, republished under its MIT licence (© rohitg00). 1,237 words, ~2,203 tokens.

Download SKILL.mdSave it as .claude/skills/learn/SKILL.md (or your agent's skills folder).
name
learn
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"
version
1.0.0
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.
  • CLAUDE-CERTIFICATION.md belongs to claude-certification.

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.

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.
Show full SKILL.md (502 more words)Show less

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 learner gets both wrong, offer to re-do that lesson instead of advancing, but let them choose.

Keep each correct option private until the learner answers. Never put a real answer letter, a likely answer, or the quiz's answer distribution in a reply-format hint. In plain text, use Reply with one letter: <A|B|C|D>.

Step 2 — Teach the lesson

Fetch the lesson's en.md. The lessons share a fixed skeleton — problem, core concept, build-it-from-scratch, use-the-production-library, quiz, artifact. Teach it in that order, interactively:

  1. Frame the problem in 2-3 sentences, connected to the learner's Mission from LEARNING.md when it fits naturally. Do not recite the file.
  2. Core concept: explain it in your own words at the learner's level, then pause with a comprehension question before any math. Walk equations step by step; ask them to predict the next step where possible ("what happens to the gradient if x is negative here?").
  3. Build it: walk the from-scratch code in chunks of 5-15 lines. For each chunk: what it does, why it exists, one prediction question. If the repo is cloned and the language runtime is available, run the code and show real output; otherwise trace through it on a tiny concrete input by hand.
  4. Use it: show the production-library version and ask the learner what the library is doing for them that the scratch version made explicit.
  5. Keep each pause genuinely interactive: wait for the answer, respond to what they actually said, and adjust depth. A learner saying "I know this, speed up" outranks the script.

Step 3 — Quiz

Fetch quiz.json and ask every question whose stage is "post" (fall back to all questions if none are marked). One at a time, lettered options, no hints. After each answer, give the verdict and the explanation from the file. Do not expose correct, the answer index, or a literal answer-letter example before the learner responds. Report the score as N/M.

Step 4 — Record

Update LEARNING.md:

  • Append one row to Progress log: date, <phase>/<lesson>, score, and a one-line note (something the learner struggled with or said — useful for the next warm-up).
  • Score below 70%: add the lesson to the Review queue with the missed topic.
  • Last lesson of a phase completed: set the phase Status to Done and suggest check-understanding <phase> for the full phase quiz, rendered with the host invocation contract.

If there is no LEARNING.md (learner declined setup), skip silently — never nag about it after Step 0.

Step 5 — Close

Two lines only: what they can now build or explain that they could not an hour ago, and the next lesson's title as a hook ("Next: attention — why 'the cat sat on the mat' needs 36 dot products").

© rohitg00, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/learn of rohitg00/ai-engineering-from-scratch.

Open the folder on GitHubat commit cdfd9df

Compare with similar skills

AI Engineering Course Tutor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AI Engineering Course Tutor compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Engineering Course Tutor this skillrohitg00/ai-engineering-from-scratch66k—~2.2kAutomated safety check: PassMIT
DeepTutor CLIHKUDS/DeepTutor41k—~2.8kAutomated safety check: PassApache-2.0
StudyVault Quiz Tutorbevibing/tutor-skills1.3k—~1.4kAutomated safety check: PassMIT
Zone of Proximal Development Practice LessonsTHU-MAIC/OpenMAIC40k—~1.1kAutomated safety check: PassMIT
Codex Skill Self-AssessmentFlorianBruniaux/claude-code-ultimate-guide6.1k—~2.3kAutomated safety check: PassCC-BY-SA-4.0
Learn Law With Rohasrohasnagpal/legal-ai-skills175—~2.5kAutomated safety check: PassMIT

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Categories

Questions about AI Engineering Course Tutor

What does AI Engineering Course Tutor do?

Teaches the next lesson of the AI Engineering from Scratch curriculum in the terminal, quizzes you at the end and records your progress. One invocation teaches one lesson interactively: the learner is expected to type, answer and run things rather than just scroll.com with no setup, teaches it section by section, runs the lesson quiz and records progress.

When should I use AI Engineering Course Tutor?

AI Engineering Course Tutor fits situations like: continuing a self-paced AI engineering course in the terminal; taking a lesson quiz and recording progress; resuming a course when several course state files exist.

How do I install AI Engineering Course Tutor in Claude Code?

Run `npx skills add rohitg00/ai-engineering-from-scratch --skill learn -a claude-code`. Or copy the skill folder (skills/learn in rohitg00/ai-engineering-from-scratch) into .claude/skills/learn in your project. Claude Code loads it when a task matches its description.

How do I install AI Engineering Course Tutor in Codex?

Run `npx skills add rohitg00/ai-engineering-from-scratch --skill learn -a codex`. Or copy the skill folder (skills/learn in rohitg00/ai-engineering-from-scratch) into .agents/skills/learn in your project. Codex loads it when a task matches its description.

Can I use AI Engineering Course Tutor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rohitg00/ai-engineering-from-scratch --skill learn -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/learn, .gemini/skills/learn, .github/skills/learn and .opencode/skills/learn in your project.

What does AI Engineering Course Tutor need to run?

SKILL.md names no scripts, command-line tools or credentials: AI Engineering Course Tutor is instructions for the agent only. Our summary lists: Network access to raw.githubusercontent.com, unless the course repository is cloned locally.

Does AI Engineering Course Tutor access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is AI Engineering Course Tutor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does AI Engineering Course Tutor use?

AI Engineering Course Tutor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Engineering Course Tutor use?

About 2.2k tokens (SKILL.md is roughly 8.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to AI Engineering Course Tutor?

Skills that share tags, products or a category with AI Engineering Course Tutor: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), StudyVault Quiz Tutor (bevibing/tutor-skills, 1.3k stars), Zone of Proximal Development Practice Lessons (THU-MAIC/OpenMAIC, 40k stars) and Codex Skill Self-Assessment (FlorianBruniaux/claude-code-ultimate-guide, 6.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Engineering Course Tutor?

rohitg00 (a GitHub user) maintains it in rohitg00/ai-engineering-from-scratch, which has 65,983 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on October 9, 2026.

Source: rohitg00/ai-engineering-from-scratch on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.