# How the start-learning Skill Onboards New Users in AI Engineering from Scratch

> Discover how the start-learning skill seamlessly onboards new users in AI Engineering from Scratch. It guides you through a four-step process to create your personalized learning plan.

- Repository: [Rohit Ghumare/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch)
- Tags: how-to-guide
- Published: 2026-09-02

---

**The start-learning skill conducts a deterministic four-step pipeline—resume check, three-question interview, placement quiz, and LEARNING.md generation—to create a personalized, persistent learning plan for every new user.**

The `start-learning` skill acts as the canonical entry point for the *AI Engineering from Scratch* curriculum, implemented in the `rohitg00/ai-engineering-from-scratch` repository. Rather than dumping users into generic content, it captures intent, assesses prior knowledge, and produces a structured [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file that persists across sessions. This approach ensures that whether you are a complete beginner or a practicing engineer, the curriculum adapts to your specific timeline and objectives.

## Host-Agnostic Invocation Options

According to the **Host invocation contract** defined in [`skills/start-learning/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/start-learning/SKILL.md), the skill supports three distinct syntaxes to accommodate different AI coding assistants.

**Codex host:**

```text
start-learning

```

**Claude Code host:**

```text
/start-learning

```

**Free-form natural language (any host):**

```text
Use start-learning to begin the course.

```

This flexibility ensures that learners can invoke the onboarding flow regardless of which AI assistant or IDE they are using.

## The Four-Step Onboarding Pipeline

The skill implements a strictly sequential workflow defined in [`skills/start-learning/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/start-learning/SKILL.md). Each step produces deterministic outputs that feed into the next phase.

### Step 1: Resume Routing and State Detection

Before initiating a fresh onboarding, the skill scans for existing state files to prevent accidental overwrites. It checks for four specific filenames: [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md), [`MCP-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/MCP-LEARNING.md), [`AGENT-SKILLS-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENT-SKILLS-LEARNING.md), and [`CLAUDE-CERTIFICATION.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/CLAUDE-CERTIFICATION.md).

If any file exists and the user requests a resume, control is immediately handed off to the owning skill (`learn`, `learn-mcp`, `learn-agent-skills`, or `claude-certification`). If [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) exists but the user wants to adjust their plan, the skill presents three options: **Resume**, **Re-run placement**, or **Start over**.

### Step 2: The Learner Interview

When no valid resume path exists, the skill executes a **three-question interview** designed to capture qualitative intent:

- **Why** do you want to study AI engineering?
- **How much time** can you devote per week?
- **What** do you hope to build by the end?

These responses are stored verbatim and later injected into the **Mission** section of the generated learning plan, ensuring the curriculum remains anchored to the learner's specific goals.

### Step 3: Placement Quiz via find-your-level

The skill delegates knowledge assessment to the companion `find-your-level` skill specified in [`skills/find-your-level/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/find-your-level/SKILL.md). This invocation presents a **10-question quiz** covering five distinct knowledge areas.

The quiz result maps to an **entry phase** (numbered 0-19) and determines which sections of the 20-phase roadmap the learner can safely skip. This placement data feeds directly into the **Placement** section of the final document, including the week-by-week pace calculation.

### Step 4: Generating the LEARNING.md File

Using the interview responses, placement outcome, and static roadmap data from [`ROADMAP.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/ROADMAP.md), the skill writes a fully-structured [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file to the project root. The file contains four critical sections:

- **Mission** – The learner's stated purpose and end-goal from the interview
- **Placement** – Date, score, entry phase, and calculated weekly pace
- **Path** – A markdown table of all 20 phases with status markers (`Skip`, `Do`, `Review`, `Done`) and estimated hours derived from [`ROADMAP.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/ROADMAP.md)
- **Progress log and Review queue** – Placeholder sections for future skill updates

The exact markdown template is defined in the *Write LEARNING.md* section of the skill specification.

### Step 5: Hand-off to Curriculum

After file generation, the skill outputs three concise lines to the terminal:

1. A summary of the entry point and total estimated hours
2. The correct `learn` invocation syntax for the host (e.g., `/learn` for Claude Code)
3. The `course-guide <topic>` invocation for jumping to specific topics

This deterministic hand-off ensures the learner knows exactly how to begin their first study session.

## Anatomy of the Generated Learning Plan

The [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file produced by the skill follows a strict schema. Here is a truncated example showing the structure:

```markdown

# My AI Engineering Path

<!-- Managed by the ai-engineering-from-scratch learning skills.
     Repo: https://github.com/rohitg00/ai-engineering-from-scratch -->

## Mission

I want to ship an AI product that can answer user questions, and I aim to build a personal assistant agent.

## Placement

- Date: 2026-09-02
- Score: 7/10
- Entry point: Phase 4: Linear Algebra
- Pace: ~5 h/week

## Path

| Phase | Name               | Status | Est. hours |
|-------|--------------------|--------|------------|
| 0     | Foundations        | Skip   | 4          |
| 1     | Linear Algebra     | Do     | 6          |
| …     | …                  | …      | …          |

```

The **Path** table references the canonical roadmap defined in [`ROADMAP.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/ROADMAP.md), ensuring that hour estimates remain consistent with the curriculum's published standards.

## Resuming Existing Progress

If [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) already exists when the skill is invoked, the onboarding flow enters a **protection mode**. Instead of overwriting the file, the skill reads the existing state and offers three explicit options:

- **Resume** – Continue from the last recorded phase
- **Re-run placement** – Retake the 10-question quiz to adjust the entry point
- **Start over** – Delete the existing plan and begin the interview fresh

This safety mechanism prevents learners from losing their progress history or personalized mission statements.

## Summary

- The `start-learning` skill supports **three invocation syntaxes** (Codex, Claude Code, and natural language) for maximum compatibility.
- It checks for **four state files** ([`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md), [`MCP-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/MCP-LEARNING.md), [`AGENT-SKILLS-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENT-SKILLS-LEARNING.md), [`CLAUDE-CERTIFICATION.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/CLAUDE-CERTIFICATION.md)) to enable seamless resume routing.
- New users complete a **three-question interview** and a **10-question placement quiz** before the system generates their plan.
- The output is a structured [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file containing **Mission**, **Placement**, and **Path** sections that persist across sessions.
- The skill sources phase data and hour estimates from [`ROADMAP.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/ROADMAP.md) and delegates quiz logic to [`skills/find-your-level/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/find-your-level/SKILL.md).

## Frequently Asked Questions

### What happens if I already have a LEARNING.md file?

If [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) exists, the skill detects it immediately and presents three options: **Resume** to continue your current path, **Re-run placement** to retake the knowledge quiz and potentially adjust your entry phase, or **Start over** to delete the existing file and begin fresh. This prevents accidental data loss.

### How does the skill determine my starting phase?

The skill invokes the `find-your-level` skill, which administers a 10-question quiz across five knowledge areas. Your score maps to one of 20 phases (0-19) defined in [`ROADMAP.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/ROADMAP.md). The result determines which phases are marked as `Skip` versus `Do` in your generated [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file.

### Can I use the start-learning skill with any AI coding assistant?

Yes. According to the **Host invocation contract** in [`skills/start-learning/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/start-learning/SKILL.md), the skill recognizes three syntaxes: bare commands for Codex (`start-learning`), slash commands for Claude Code (`/start-learning`), and natural language prompts for generic hosts.

### Where does the skill store my interview answers?

The skill writes your responses to the **Mission** section of [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) verbatim. This ensures that throughout the 20-phase curriculum, your original goals and time constraints remain visible at the top of your learning plan, helping the system prioritize relevant topics.