# How Claude Agent Skills Find Your Level and Check Understanding in AI Engineering From Scratch > Discover how Claude agent skills like find-your-level and check-understanding personalize AI engineering learning paths with interactive quizzes. Start your journey today. - Repository: [Rohit Ghumare/ai-engineering-from-scratch](https://github.com/rohitg00/ai-engineering-from-scratch) - Tags: deep-dive - Published: 2026-06-18 --- **The `find-your-level` and `check-understanding` skills in the rohitg00/ai-engineering-from-scratch repository operate as automated assessment tools that map learners to personalized curriculum paths through interactive quizzes.** The **Claude agent skills** embedded in this open-source curriculum provide intelligent tutoring capabilities that eliminate the guesswork from self-paced learning. According to the source code in `.claude/skills/`, these tools leverage structured quiz logic and content sampling to deliver precise placement recommendations and mastery verification across a 20-phase, 260-lesson learning track. ## How the find-your-level Skill Operates The **find-your-level** skill functions as a diagnostic placement engine that maps total quiz scores to specific entry points in the curriculum. ### Ten-Question Placement Quiz Structure The skill administers a **ten-question assessment** covering five knowledge domains: **Math & Statistics**, **Classical ML**, **Deep Learning**, **NLP & Transformers**, and **Applied AI**. Each domain contains exactly two questions, presented in sequential rounds. Learners score 0–2 points per area, yielding a composite score between 0 and 10. This granular scoring allows the system to distinguish between surface familiarity and deep competency across the AI engineering stack. ### Score-to-Phase Mapping Logic Inside [`.claude/skills/find-your-level/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/.claude/skills/find-your-level/SKILL.md), lines **72–80** define the mapping table that converts raw scores into curriculum phases. For example, a score of 0–3 routes the learner to **Phase 1: Math Foundations**, while a perfect 10 advances them directly to **Phase 14: Agent Engineering**. The mapping logic uses inclusive ranges to ensure learners start at the appropriate difficulty threshold without redundant review of mastered material. ### Personalized Learning Path Generation After calculating the entry phase, the skill constructs a markdown compatibility table spanning all 20 phases. As implemented in lines **84–122** of the skill definition, the logic marks each phase as **Skip**, **Review**, or **Do**, and injects hour estimates pulled dynamically from [`ROADMAP.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/ROADMAP.md). The output includes the completed table, a total-hour estimate for the remaining curriculum, and a concise recommendation on the optimal starting point. ## How the check-understanding Skill Operates The **check-understanding** skill provides **phase-level mastery verification** through adaptive content sampling and interactive questioning. ### Activation and Phase Resolution Users trigger the skill via `/check-understanding ` or natural language equivalents like "quiz me on phase 2". The command parser, defined in lines **13–19** of [`.claude/skills/check-understanding/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/.claude/skills/check-understanding/SKILL.md), accepts phase numbers (0–19) or canonical names. A resolution table in lines **31–50** validates inputs against the `phases//` directory structure, returning specific error messages for invalid entries like "quantum" or out-of-range indices. ### Content Sampling and Question Generation The skill ingests lesson content using a glob pattern to locate [`docs/en.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/docs/en.md) files within the target phase directory. For large phases, it implements **strategic sampling**—reading the first, middle, and last lessons to maintain quiz brevity while covering the full scope (lines **58–63**). Question generation occurs in lines **64–84**, producing exactly **eight multiple-choice items**: four conceptual (**What/Why**) and four practical (**How/Build**). Each question includes 3–4 distractors, a single correct answer, and a metadata tag linking back to the originating lesson file for remediation purposes. ### Interactive Delivery and Scoring Delivery leverages the `AskUserQuestion` tool (lines **86–101**), presenting questions one-by-one and awaiting learner responses before proceeding. The scoring engine (lines **111–138**) assigns categorical grades based on accuracy: - **Mastered** (7–8 correct) - **Almost** (5–6 correct) - **Developing** (3–4 correct) - **Start Over** (0–2 correct) Feedback includes a breakdown of missed items with explanations and direct links to the relevant lesson files. Lines **141–148** handle next-step logic, offering options to retake the quiz, attempt a different phase, or request targeted explanations. ## Usage Examples ```markdown # Placement quiz invocation User: /find-your-level Claude: Welcome! Let's find the right starting point for you. ... (Claude administers 10 questions across 5 domains) ... ``` ```markdown # Phase understanding check User: /check-understanding 3 Claude: Great! We'll test your knowledge of Deep Learning Core. 1/8 (Conceptual) – What does back-propagation compute? A) Learning rate B) Gradient of loss C) Number of layers D) Batch size > User: B ... (Claude continues with remaining questions) ... ``` ```markdown # Error handling for invalid phases User: /check-understanding quantum Claude: Unknown phase 'quantum'. Pick from the list below: 0 Setup & Tooling, 1 Math Foundations, ..., 19 Capstone Projects ``` ## Key Implementation Files - **[`.claude/skills/find-your-level/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/.claude/skills/find-your-level/SKILL.md)** – Contains the placement quiz logic, score mapping (lines 72–80), and personalized table generation (lines 84–122) - **[`.claude/skills/check-understanding/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/.claude/skills/check-understanding/SKILL.md)** – Houses the phase resolution table, content sampling algorithm, and interactive questioning system - **[`README.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/README.md)** – Lists available skill commands and quick-start references - **[`ROADMAP.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/ROADMAP.md)** – Provides hour estimates consumed by the `find-your-level` skill for learning path calculations - **`phases//docs/en.md`** – Source lesson files sampled by the `check-understanding` skill ## Summary - **The `find-your-level` skill** uses a 10-question, 5-domain assessment to generate scores between 0–10, mapping results to 20 possible curriculum phases via a defined table in lines 72–80 of its SKILL.md file. - **The `check-understanding` skill** validates phase inputs against a directory mapping table (lines 31–50), samples lesson content strategically, and delivers 8 adaptive questions (4 conceptual, 4 practical) using the `AskUserQuestion` tool. - Both skills output structured markdown tables, provide specific file-path references for remediation, and integrate with the repository's [`ROADMAP.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/ROADMAP.md) for hour estimation and curriculum navigation. ## Frequently Asked Questions ### How does the find-your-level skill determine which curriculum phase to recommend? The skill calculates a 0–10 point total across five knowledge areas, then consults a hardcoded mapping table in lines 72–80 of [`.claude/skills/find-your-level/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/.claude/skills/find-your-level/SKILL.md). Score ranges correspond to specific phases—for instance, 4–5 points route to Phase 3 (Deep Learning Core), while a perfect 10 advances the learner to Phase 14 (Agent Engineering). ### What happens if I request a quiz for a phase that doesn't exist in check-understanding? The skill parses your input against a resolution table defined in lines 31–50 of [`.claude/skills/check-understanding/SKILL.md`](https://github.com/rohitg00/ai-engineering-from-scratch/blob/main/.claude/skills/check-understanding/SKILL.md). If the phase name or number is invalid, it returns a helpful error message listing all valid options from 0 (Setup & Tooling) through 19 (Capstone Projects). ### How many questions does each skill generate, and what types are they? The **find-your-level** skill generates exactly **10 questions**—two per knowledge domain (Math & Statistics, Classical ML, Deep Learning, NLP & Transformers, Applied AI). The **check-understanding** skill generates **8 questions** per phase: four conceptual (What/Why) and four practical (How/Build), each with 3–4 multiple-choice options. ### Can the check-understanding skill handle phases with many lessons? Yes. According to lines 58–63 of the skill definition, when processing large phases, the skill implements **content sampling**—it reads the first, middle, and last lesson files within the phase directory to generate questions without overwhelming the context window or the learner.