/start-learning

name: start-learning
version: 1.0.0
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"
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 other `skill-name`

forms, or tell the learner to choose the skill from `/skills`.

• Claude Code: `/start-learning`, `/learn`, `/course-guide`, and other

`/skill-name` forms.

• 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.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, 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 `learn` with 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>.md` as an archive, then proceed with the full onboarding below. Never delete or overwrite their history silently.

Step 1: The interview (3 questions, keep it short)

1. **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. 2. **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. 3. **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