Hung-Yi Lee Teaching Style
voidful/hung-yi-lee-skill
Explains machine learning, LLMs, AI agents and speech modeling in a Hung-Yi Lee-inspired teaching style, drawing on a knowledge base built from his lectures and research references.
Onboards a learner into the AI Engineering from Scratch curriculum with an interview and placement quiz, and writes a persistent LEARNING.md study plan.
Install AI Engineering Course Onboarding
$ npx skills add rohitg00/ai-engineering-from-scratch --skill start-learning -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install rohitg00/ai-engineering-from-scratch start-learning --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratch.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/start-learning .claude/skills/start-learning && rm -rf skills-srcUse ~/.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/
Install the "start-learning" agent skill from https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/skills/start-learning into .claude/skills/start-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "start-learning", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/skills/start-learningType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add rohitg00/ai-engineering-from-scratch --skill start-learning -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install rohitg00/ai-engineering-from-scratch start-learning --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratch.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/start-learning .agents/skills/start-learning && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "start-learning" agent skill from https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/skills/start-learning into .agents/skills/start-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "start-learning", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add rohitg00/ai-engineering-from-scratch --skill start-learning -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install rohitg00/ai-engineering-from-scratch start-learning --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratch.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/start-learning .cursor/skills/start-learning && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "start-learning" agent skill from https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/skills/start-learning into .cursor/skills/start-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "start-learning", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/rohitg00/ai-engineering-from-scratch.git --path skills/start-learning--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add rohitg00/ai-engineering-from-scratch --skill start-learning -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install rohitg00/ai-engineering-from-scratch start-learning --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratch.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/start-learning .gemini/skills/start-learning && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "start-learning" agent skill from https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/skills/start-learning into .gemini/skills/start-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "start-learning", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install rohitg00/ai-engineering-from-scratch start-learningInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add rohitg00/ai-engineering-from-scratch --skill start-learning -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratch.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/start-learning .github/skills/start-learning && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "start-learning" agent skill from https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/skills/start-learning into .github/skills/start-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "start-learning", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add rohitg00/ai-engineering-from-scratch --skill start-learning -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install rohitg00/ai-engineering-from-scratch start-learning --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratch.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/start-learning .opencode/skills/start-learning && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "start-learning" agent skill from https://github.com/rohitg00/ai-engineering-from-scratch/tree/main/skills/start-learning into .opencode/skills/start-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "start-learning", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
start-learning- GitHub stars
- 66k
- Token cost
- ~2k tokens
- SKILL.md length
- 1,011 words
- Files
- 1
- Skills in repo
- 16
- Repo updated
- First seen
- Licence
- MIT
At a glance
Onboards a learner into the AI Engineering from Scratch curriculum with an interview and placement quiz, and writes a persistent LEARNING.md study plan.
- Works in 4 steps: The interview (3 questions, keep it short) → Placement → Write LEARNING.md → …
- Starting the AI Engineering from Scratch course for the first time
- SKILL.md covers Host invocation contract, Resume routing across course…, Focused MCP handoff and Focused Agent Skills handoff, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
This one-time onboarding skill sets up a learner for the AI Engineering from Scratch curriculum, 523 lessons across 20 phases from linear algebra to autonomous agents. The agent interviews the learner about why they are learning and where to start, runs a placement quiz, and writes LEARNING.md in the current directory. Later sessions of the learn skill read and update that file, so it acts as the learner's source of truth.
It works with any agent, using a structured question tool when one exists and lettered plain-text options otherwise, and it shows next-step commands in the host's own syntax, since Codex, Claude Code and other hosts invoke skills differently. Before generic onboarding it resolves resume or continue requests against other state files: LEARNING.md for the full curriculum, MCP-LEARNING.md for the MCP route, and AGENT-SKILLS-LEARNING.md and CLAUDE-CERTIFICATION.md for their own routes. It dispatches to the right owner and asks which route to resume when several exist.
When your agent uses it
- Starting the AI Engineering from Scratch course for the first time
- Creating a personalized learning plan from a placement quiz
- Resuming a course route when several learning files exist
Example prompts
- “Start learning: set up the AI engineering course for me.”
- “Run the placement quiz and create my learning plan.”
- “Continue my course where I left off.”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 7a181b4. 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 (its code samples are markdown).
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 Onboarding loads about 2k tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 1,011 words of instructions outside code blocks.
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 7a181b4, republished under its MIT licence (© rohitg00). 1,011 words, ~2,049 tokens.
Download SKILL.mdSave it as
.claude/skills/start-learning/SKILL.md (or your agent's skills folder).- name
- start-learning
- description
- One-time onboarding for the AI Engineering from Scratch curriculum (523 lessons, 20 phases). Interviews the learner, runs the placement quiz, and writes LEARNING.md, a persistent study plan the learn skill drives. Trigger phrases: "start learning", "set up the course", "begin the curriculum", "onboard me", "create my learning plan"
- version
- 1.0.0
- tags
- onboarding, curriculum, ai-engineering, learning-plan
Start Learning
You are onboarding a learner into the AI Engineering from Scratch
curriculum: 523 lessons across 20 phases, from linear algebra to autonomous
agents. Your job is to produce LEARNING.md, a single file in the current
directory that captures why they are learning, where they should start, and
what their path looks like. Every later learn session reads and updates
this file, so treat it as the learner's source of truth.
Works with any agent. If your environment has a structured question/option tool, use it for every question; otherwise present lettered options as plain text and wait for the reply.
Host invocation contract
Skill names are portable, but invocation syntax belongs to the host. Before showing a next command, use the correct form:
- Codex:
start-learning,learn,course-guide, and otherskill-nameforms, or tell the learner to choose the skill from/skills. - Claude Code:
/start-learning,/learn,/course-guide, and other/skill-nameforms. - Other compatible hosts: natural language such as
Use learn to start my first lesson.
Never present a Claude Code slash command as universal syntax. When the host is unknown, use the natural-language form.
Resume routing across course modes
Before generic onboarding, resolve every "resume" or "continue" request against these supported state files and their route owners:
LEARNING.mdbelongs tolearnfor the full curriculum.MCP-LEARNING.mdbelongs tolearn-mcpfor the Model Context Protocol (MCP) route.MCP-ENGINEERING-LEARNING.mdis the legacy filename for that samelearn-mcproute, not a separate route.AGENT-SKILLS-LEARNING.mdbelongs tolearn-agent-skills.CLAUDE-CERTIFICATION.mdbelongs toclaude-certification.
If the learner names a route in a resume or continue request, dispatch to its owner immediately even when other state files exist, then 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, invoke it and stop this skill before generic onboarding. 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 running placement or changing any state. If none exist, continue with
generic onboarding. 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 explicitly wants Model Context Protocol (MCP) rather than the
full course, do not run placement and do not create LEARNING.md. Route to
the portable skill learn-mcp, whose source is
learning-paths/model-context-protocol.json and whose state file is
MCP-LEARNING.md. Use learn-mcp in Codex,
/learn-mcp in Claude Code, or ask another compatible host to use
learn-mcp. The dedicated tutor owns lesson selection, wire
evidence, and the public-deployment security gate.
Focused Agent Skills handoff
If the learner explicitly wants Agent Skills instead of the full course, or
AGENT-SKILLS-LEARNING.md exists and they ask to resume that route, do not run
placement and do not create LEARNING.md. Route to the portable skill
learn-agent-skills, whose source is learning-paths/agent-skills.json and
whose state file is AGENT-SKILLS-LEARNING.md. Use learn-agent-skills in
Codex, /learn-agent-skills in Claude Code, or ask another compatible host to
use learn-agent-skills. 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.
If LEARNING.md already exists, do not overwrite it. Summarize what it says
(mission, entry point, progress so far) and offer exactly three paths:
- Resume: invoke
learnwith the host syntax above; skip the interview and placement entirely. - Re-run placement: administer the quiz again, then update only the Placement section and the Path statuses; keep the Mission, the Progress log, and the Review queue untouched.
- Start over: only after an explicit confirmation, rename the current
file to
LEARNING-<YYYY-MM-DD>.mdas an archive, then proceed with the full onboarding below. Never delete or overwrite their history silently.
Show full SKILL.md (361 more words)Show less
Step 1: The interview (3 questions, keep it short)
- Why are you learning AI engineering? Free text. Examples to offer: ship an AI product, career change, understand what I already use daily, research. Capture their answer in their own words because it grounds every future lesson explanation.
- How much time per week? Options: ~2 h, ~5 h, ~10 h, "as fast as possible". Used only to phrase the pace honestly, never to cut content.
- What do you most want to build by the end? One line. An agent, a trained model, a RAG product, "not sure yet" is fine.
Do not ask more than these three. The placement quiz measures knowledge; the interview only captures intent.
Step 2: Placement
Run the placement quiz from the find-your-level skill (it installs
alongside this one): 5 areas, 10 questions, mapped to an entry phase. Preserve
that skill's answer-isolation contract: do not preload later answer-key rounds
or replace neutral <letter> placeholders with real option letters.
If the learner says they already know where they want to start ("just start
me at phase 7"), respect that and skip the quiz, with the same output
contract as a quiz run so the learn tutor always finds a well-formed plan:
- Validate the phase is 0-19 and resolve its canonical name; if it does not resolve, list the 20 phases and ask them to pick.
- In the Path table: phases below the entry point are
Skip, the entry point and everything above areDo(noReviewrows because there are no area scores to infer them from), and the Est. hours total is the sum of theDorows. - In the Placement section write
Score: self-selectedinstead of a number.
Step 3: Write LEARNING.md
Create LEARNING.md in the current directory with exactly these sections:
# My AI Engineering Path
<!-- Managed by the ai-engineering-from-scratch learning skills.
Repo: https://github.com/rohitg00/ai-engineering-from-scratch -->
## Mission
<their answer to question 1, in their words, plus the build goal from question 3>
## Placement
- Date: <YYYY-MM-DD>
- Score: <total>/10 with the area breakdown, or exactly `self-selected` when the quiz was skipped
- Entry point: Phase <N>: <name>
- Pace: ~<hours>/week
## Path
| Phase | Name | Status | Est. hours |
|-------|------|--------|------------|
<all 20 phases; Status is Skip, Review, Do, or Done from the placement
result. Hours come from ROADMAP.md: read it locally if the repo is cloned,
otherwise fetch
https://raw.githubusercontent.com/rohitg00/ai-engineering-from-scratch/main/ROADMAP.md>
## Progress log
| Date | Lesson | Quiz | Note |
|------|--------|------|------|
## Review queue
<empty for now; learn adds lessons the quizzes flag>Step 4: Hand off
Close with three lines, nothing more:
- Their entry point and total estimated hours for the Review + Do phases.
- Give the host-correct invocation for
learnand say that it starts the first lesson and picks up from this file every time. - Give the host-correct invocation for
course-guide <topic>and say that it can jump to a specific topic instead.
© 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/start-learning of rohitg00/ai-engineering-from-scratch.
Open the folder on GitHubat commit 7a181b4
Compare with similar skills
AI Engineering Course Onboarding 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| AI Engineering Course Onboarding this skillrohitg00/ai-engineering-from-scratch | 66k | — | ~2k | Automated safety check: Pass | MIT | |
| Hung-Yi Lee Teaching Stylevoidful/hung-yi-lee-skill | 1.3k | — | ~13k | Automated safety check: Pass | None | |
| Learnfancyboi999/ai-engineering-from-scratch-zh | 1.2k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Workshopbrevdev/workshop-build-an-agent | 144 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Teachentireio/skills | 223 | — | ~1.7k | Automated safety check: Pass | MIT | |
| Workshopbrevdev/workshop-build-an-agent | 144 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 |
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Related
Categories
Questions about AI Engineering Course Onboarding
What does AI Engineering Course Onboarding do?
Onboards a learner into the AI Engineering from Scratch curriculum with an interview and placement quiz, and writes a persistent LEARNING.md study plan. This one-time onboarding skill sets up a learner for the AI Engineering from Scratch curriculum, 523 lessons across 20 phases from linear algebra to autonomous agents.md in the current directory.
When should I use AI Engineering Course Onboarding?
AI Engineering Course Onboarding fits situations like: starting the AI Engineering from Scratch course for the first time; creating a personalized learning plan from a placement quiz; resuming a course route when several learning files exist.
How do I install AI Engineering Course Onboarding in Claude Code?
Run `npx skills add rohitg00/ai-engineering-from-scratch --skill start-learning -a claude-code`. Or copy the skill folder (skills/start-learning in rohitg00/ai-engineering-from-scratch) into .claude/skills/start-learning in your project. Claude Code loads it when a task matches its description.
How do I install AI Engineering Course Onboarding in Codex?
Run `npx skills add rohitg00/ai-engineering-from-scratch --skill start-learning -a codex`. Or copy the skill folder (skills/start-learning in rohitg00/ai-engineering-from-scratch) into .agents/skills/start-learning in your project. Codex loads it when a task matches its description.
Can I use AI Engineering Course Onboarding 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 start-learning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/start-learning, .gemini/skills/start-learning, .github/skills/start-learning and .opencode/skills/start-learning in your project.
What does AI Engineering Course Onboarding need to run?
SKILL.md names no scripts, command-line tools or credentials: AI Engineering Course Onboarding is instructions for the agent only.
Does AI Engineering Course Onboarding 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 Onboarding 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 Onboarding use?
AI Engineering Course Onboarding 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 Onboarding use?
About 2k tokens (SKILL.md is roughly 8.2k 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 Onboarding?
Skills that share tags, products or a category with AI Engineering Course Onboarding: Hung-Yi Lee Teaching Style (voidful/hung-yi-lee-skill, 1.3k stars), Learn (fancyboi999/ai-engineering-from-scratch-zh, 1.2k stars), Workshop (brevdev/workshop-build-an-agent, 144 stars) and Teach (entireio/skills, 223 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 Onboarding?
rohitg00 (a GitHub user) maintains it in rohitg00/ai-engineering-from-scratch, which has 65,647 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on October 6, 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.