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rohitg00/ai-engineering-from-scratch860 installs

learn

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"

How do I install this agent skill?

npx skills add https://github.com/rohitg00/ai-engineering-from-scratch --skill learn
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The skill functions as an interactive tutor that fetches curriculum data from a GitHub repository and tracks student progress in a local file. The primary security consideration is the indirect prompt injection surface created by processing and executing code snippets from external sources, which is an inherent risk in such AI-driven educational tools.

  • Socketpass

    No alerts

  • Snykwarn

    Risk: MEDIUM · 2 issues

What does this agent skill do?

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.

Content sources

Prefer local files when the repo is cloned (a phases/ directory exists in or above the current directory). Otherwise fetch from:

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)

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, take /check-understanding on a phase of their choice, or re-run /start-learning to extend the plan into skipped phases.
  • Missing: say that /start-learning builds a personalized plan, 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 learner gets both wrong, offer to re-do that lesson instead of advancing, but let them choose.

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. 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.

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").

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/rohitg00/ai-engineering-from-scratch/learn">View learn on skillZs</a>