# What Is the Purpose of the `learn` Skill in the AI Engineering from Scratch Curriculum? > Discover the purpose of the learn skill in AI Engineering from Scratch. It acts as an interactive tutor, managing lessons and learner progress within the repository. - Repository: [Rohit Ghumare/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) - Tags: getting-started - Published: 2026-09-02 --- **The `learn` skill functions as the interactive tutor that orchestrates lesson delivery, manages resume routing across specialized learning paths, and persists learner progress in the AI Engineering from Scratch repository.** The `learn` skill is the central pedagogical engine in RohitG00's *AI Engineering from Scratch*. It transforms static curriculum content into a personalized, state-driven educational experience by determining what to teach next, how to deliver it interactively, and where to store learning evidence for future sessions. ## Core Responsibilities of the Learn Skill ### Orchestrating Lesson Delivery via the Six-Step Workflow According to [`skills/learn/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/learn/SKILL.md) (lines 49-83), the skill implements a rigid **Step 0-5 sequence** that structures every learning session: 1. **Step 0**: Route selection—checks for existing state files and dispatches to sub-skills if needed 2. **Step 1**: Warm-up recall—presents two random quiz questions from the previous lesson 3. **Step 2**: Lesson loading—fetches the next unfinished lesson from `phases///docs/en.md` 4. **Step 3**: Interactive delivery—walks through problem framing, core concepts, from-scratch implementation, and production-library comparisons 5. **Step 4**: Progress recording—appends session data to [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) (lines 63-71) 6. **Step 5**: Next steps—determines whether to continue, review, or pause ### Managing Resume Routing and Sub-Skills Before initiating a lesson, the skill inspects the filesystem for route-specific state files including [`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), and [`AGENT-SKILLS-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENT-SKILLS-LEARNING.md). As implemented in lines 49-73 of [`SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/SKILL.md), if the learner wishes to resume a specialized path, the skill dispatches to dedicated sub-skills such as `learn-mcp` or `learn-agent-skills`, sourcing their curricula from [`learning-paths/model-context-protocol.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/learning-paths/model-context-protocol.json) and [`learning-paths/agent-skills.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/learning-paths/agent-skills.json) respectively. ### Providing Warm-Up Recall Questions To prime the learner's memory, **Step 1** of the workflow (lines 20-27) automatically selects two random quiz questions from the previously completed lesson before introducing new material. This spaced repetition mechanism ensures knowledge retention across sessions. ### Recording Progress and State Management The skill maintains a comprehensive learning journal. At **Step 4** (lines 63-71), it appends a structured row to [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) containing the date, lesson identifier, quiz score, and qualitative notes. It also updates phase status indicators and review queues, enabling precise session restoration. ### Supporting Multiple Host Invocation Syntaxes The skill defines a flexible **host invocation contract** (lines 22-30) that accommodates different execution environments. Whether running in Codex, Claude Code, or natural-language interfaces, the skill accepts `learn`, `/learn`, or conversational requests like "Use learn to start the AI engineering course." ### Acting as the Curriculum Gateway The `learn` skill serves as the entry point for the entire educational ecosystem. As noted in lines 15-19 of [`SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/SKILL.md), when learners require personalized roadmaps, the skill can invoke the `start-learning` sub-skill to generate customized learning plans before transitioning into standard lesson delivery. ## How the Learn Skill Retrieves Curriculum Content The skill dynamically assembles lessons by reading from the structured `phases/` directory: ```python # Conceptual content retrieval pattern based on SKILL.md implementation lesson_content = f"phases/{current_phase}/{next_lesson}/docs/en.md" quiz_data = f"phases/{current_phase}/{next_lesson}/quiz.json" ``` It parses the lesson's markdown to extract sections covering problem framing, core concepts, from-scratch implementation, and production-library comparisons, then presents the quiz questions defined in the accompanying JSON. ## Practical Usage Examples The `learn` skill operates across multiple host environments with syntax adaptations: ```text # Codex-style native command learn ``` ```text # Claude Code slash-command style /learn ``` ```text # Natural-language interface (any host) Use learn to start the AI engineering course. ``` When invoked, the skill first attempts to read [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) to locate the learner's current position. If no state exists, it initializes a new learning session starting at Phase 1, Lesson 1. ## Integration with Specialized Learning Paths The architecture supports branching curricula through dedicated state files: | State File | Sub-Skill | Source JSON | |------------|-----------|-------------| | [`MCP-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/MCP-LEARNING.md) | `learn-mcp` | [`learning-paths/model-context-protocol.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/learning-paths/model-context-protocol.json) | | [`AGENT-SKILLS-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENT-SKILLS-LEARNING.md) | `learn-agent-skills` | [`learning-paths/agent-skills.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/learning-paths/agent-skills.json) | This routing logic enables the `learn` skill to function as a universal router, directing learners to domain-specific tutors while maintaining centralized progress tracking. ## Summary - The **`learn` skill** acts as an interactive tutor that manages the complete lifecycle of a learning session in the *AI Engineering from Scratch* curriculum. - It implements a **six-step workflow** (Steps 0-5) defined in [`skills/learn/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/learn/SKILL.md) that handles everything from resume routing to progress persistence. - The skill maintains learner state in **[`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md)** and supports specialized paths through separate state files and sub-skills. - It provides **spaced repetition** through warm-up quiz questions and supports multiple invocation syntaxes across different AI coding hosts. - Content is dynamically fetched from the `phases/` directory, combining lesson documentation ([`en.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/en.md)) with interactive assessments ([`quiz.json`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/quiz.json)). ## Frequently Asked Questions ### How does the `learn` skill know which lesson to teach next? The skill reads the learner's state file, [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md), to identify the most recently completed lesson and selects the next sequential lesson from the curriculum structure defined in the `phases/` directory. If no state file exists, it begins with Phase 1, Lesson 1. ### Can the `learn` skill handle multiple specialized learning tracks? Yes. Before starting a lesson, the skill checks for route-specific state files such as [`MCP-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/MCP-LEARNING.md) or [`AGENT-SKILLS-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENT-SKILLS-LEARNING.md). If these exist and the learner chooses to resume that path, the skill dispatches to specialized sub-skills (`learn-mcp` or `learn-agent-skills`) while referencing their respective JSON curriculum definitions in the `learning-paths/` directory. ### What information does the `learn` skill store after each session? According to lines 63-71 of [`skills/learn/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/learn/SKILL.md), the skill appends a row to [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) containing the session date, lesson identifier, quiz score, and a brief qualitative note. It also updates phase completion status and review queue indicators to facilitate spaced repetition in future sessions. ### How can I invoke the `learn` skill in different AI coding environments? The skill supports a **host invocation contract** (lines 22-30) that accommodates various syntaxes: native commands (`learn`), slash commands (`/learn`), or natural language requests. The specific format depends on whether you are using Codex, Claude Code, or another compatible AI assistant host.