# How to Use the start-learning Skill for Onboarding in AI Engineering from Scratch > Master AI engineering onboarding with the start-learning skill. Get a personalized curriculum and track your progress efficiently. Start learning today! - Repository: [Rohit Ghumare/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) - Tags: getting-started - Published: 2026-08-30 --- **The `start-learning` skill creates a personalized onboarding plan by conducting a brief interview, running a placement quiz, and generating a [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file that serves as the single source of truth for your curriculum progress.** The **AI Engineering from Scratch** curriculum by `rohitg00/ai-engineering-from-scratch` provides a structured, self-paced learning environment through modular skills. The `start-learning` skill serves as the entry point for all new learners, establishing your learning profile and determining your optimal entry phase into the course material. ## Host-Specific Invocation Syntax The skill follows a **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) (lines 26-35) that adapts to different interfaces. The metadata header (lines 1-10) declares the trigger phrases that activate the skill across supported hosts. Depending on your environment, invoke the skill using one of these patterns: - **Codex-compatible hosts**: `start-learning` - **Claude Code-compatible hosts**: `/start-learning` - **Plain-language interfaces**: "Use start-learning to begin the course." This contract ensures the skill works across any host—whether you are using a CLI, integrated development environment, or natural-language chat interface—without requiring code changes to the underlying skill logic. ## Resume Routing and State Detection Before initiating a fresh onboarding flow, the skill checks for existing learning state files through **resume routing** logic documented at lines 41-64 in [`SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/SKILL.md). This prevents redundant onboarding for returning learners. The skill searches for files such as [`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), or [`AGENT-SKILLS-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/AGENT-SKILLS-LEARNING.md). If detected, the skill routes you directly to the appropriate downstream skill—`learn`, `learn-mcp`, or `learn-agent-skills`—rather than re-running the placement interview. This architecture ensures **state-file ownership** remains clean: `start-learning` only modifies [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md), while other learning routes maintain their own separate state files (lines 46-52). ## The Onboarding Flow The `start-learning` skill executes a four-stage pipeline to establish your learning environment. ### 1. The Placement Interview (Lines 105-115) The skill prompts you with three structured questions to ground future explanations: - Why are you learning AI engineering? - How many hours can you devote each week? - What do you most want to build by the end of the curriculum? Your answers are stored verbatim in the generated state file to personalize subsequent lessons. ### 2. The find-your-level Placement Quiz Immediately following the interview, the skill invokes the companion **`find-your-level`** skill (line 21) to administer a **5-area, 10-question assessment**. This quiz determines your **entry phase** and populates the Path table in your learning plan. ### 3. Generating the LEARNING.md State File Section 39-71 of [`SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/SKILL.md) defines a strict markdown template (lines 43-71) that creates your [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) with four mandatory sections: - **Mission**: Your stated goals from the interview - **Placement**: Results from the `find-your-level` quiz - **Path**: Structured table of phases with estimated hours drawn from [`ROADMAP.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/ROADMAP.md) - **Progress log and Review queue**: Empty containers for ongoing tracking The template embeds a permanent link back to the repository (line 45) for reference. ### 4. Hand-off to Learning Mode After writing [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md), the skill outputs three concise lines (lines 75-81): 1. A summary of your entry point and estimated total hours 2. The correct host-specific invocation for the `learn` skill 3. The invocation pattern for `course-guide ` to jump to specific subjects ## State-File Ownership and Persistence Each learning route owns its own state file to prevent **cross-route contamination** (lines 46-52). The `start-learning` skill strictly manages only [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md); if you later switch to the MCP or Agent Skills tracks, those routes respectively create [`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). This separation ensures that your progress in one curriculum mode does not accidentally overwrite or interfere with another. The [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file serves as the single source of truth for the standard learning path, readable by subsequent `learn` sessions to maintain continuity across disconnected study sessions. ## Practical Usage Examples Initialize onboarding on a Codex-compatible CLI: ```bash start-learning ``` Begin the curriculum in Claude Code: ```bash /claude-code /start-learning ``` For hosts without slash-command support, use natural language: ``` Use start-learning to begin the course. ``` Typical interaction flow: ``` User: /start-learning Assistant: Welcome! Why are you learning AI engineering? User: To build a personal AI assistant. Assistant: How many hours can you devote each week? User: ~5 h. Assistant: What do you most want to build by the end? User: An autonomous chatbot. Assistant: Running the placement quiz… (invokes find-your-level) Assistant: Writing LEARNING.md… Assistant: Done! Your entry point is Phase 3 – "Neural Networks". Estimated total: 120 h. Next step: run `learn` (or `/learn`) to start the first lesson. You can also jump to a specific topic with `course-guide `. ``` ## Extensibility and Host Compatibility Because the skill relies solely on **plain-text I/O** and a well-known file name ([`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md)), future hosts can adopt the same contract without code modifications. The natural-language fallback (line 35) guarantees graceful degradation on hosts that do not support structured slash commands. The architecture separates host-specific rendering from core skill logic, meaning the interview questions, quiz invocation, and file generation remain consistent regardless of whether you are using Codex, Claude Code, or a custom interface. ## Summary - **The `start-learning` skill** is the mandatory entry point for the AI Engineering from Scratch curriculum, defined in [`skills/start-learning/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/skills/start-learning/SKILL.md). - It supports **multiple host syntaxes**: bare commands for Codex, slash commands for Claude Code, and natural language for generic interfaces. - The skill performs **resume routing** to detect existing [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) files and avoid redundant onboarding. - The onboarding flow includes a **3-question interview**, invocation of the **`find-your-level`** placement quiz, and generation of a structured [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file. - **State-file ownership** is strictly partitioned: `start-learning` only writes to [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md), while other learning modes use their own distinct state files. - The skill hands off to the `learn` command with host-specific syntax and provides access to the `course-guide` utility for topic-specific navigation. ## Frequently Asked Questions ### How do I restart the onboarding process if I want to change my goals? Delete the [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) file from your workspace root and invoke the `start-learning` skill again. Because the skill checks for existing state files before running the interview, removing [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) triggers a fresh onboarding flow. Your previous progress in other tracks (such as [`MCP-LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/MCP-LEARNING.md)) remains unaffected due to strict state-file separation. ### What happens if I already have a LEARNING.md file from a different course? The `start-learning` skill specifically checks the structure and metadata within [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) to determine if it belongs to the AI Engineering from Scratch curriculum. If the file indicates you are mid-progress, the skill routes you to the appropriate resume logic (lines 41-64) rather than overwriting your existing plan. To force a reset, manually archive or delete the existing file. ### Can I use the start-learning skill without Codex or Claude Code? Yes. The skill implements a **natural-language fallback** (line 35) that allows any host supporting plain text I/O to invoke the skill. Simply type "Use start-learning to begin the course" in compatible chat interfaces. The skill generates the same [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) structure regardless of host, ensuring consistent behavior across environments. ### Where does the estimated hours information come from in my learning plan? The **Path** table in your generated [`LEARNING.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/LEARNING.md) pulls estimated hours from [`ROADMAP.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/ROADMAP.md) in the repository root. During file generation (lines 43-71), the skill maps your placement results—determined by the `find-your-level` quiz—to the corresponding phases in the roadmap, calculating your total estimated commitment based on the interview responses regarding weekly availability.