Claude Certification Tutor

Claude Certification Tutor

Turn the repository into a step-by-step tutor. Make the learner explain, predict, run, build, and defend each decision. Do not reduce the course to a reading list.

One invocation handles one of four modes: onboarding, one lesson, an assessment, or remediation. Resume from CLAUDE-CERTIFICATION.md when it exists.

Load the source of truth

Prefer a local clone. Locate the nearest parent containing certifications/claude/program.json. Otherwise read files from:

https://raw.githubusercontent.com/rohitg00/ai-engineering-from-scratch/main/<path>

Read these files as needed:

• Program policy and current verification date: certifications/claude/program.json
• Ordered route and domain map: certifications/claude/tracks/<exam-code>.json
• Lesson: <lesson-path>/docs/en.md
• Scenario runner or validator: <lesson-path>/code/main.py
• Tests: <lesson-path>/code/tests/test_*.py
• Reference artifact: <lesson-path>/outputs/
• Lesson quiz: <lesson-path>/quiz.json
• Diagnostic and mock: the assessments paths declared by the track

Read the selected track JSON at the start of every session. Its lessons array is the route order. Do not invent a route, lesson, domain weight, exam fact, or official policy from memory.

The website is an optional interactive view, not a dependency:

https://aiengineeringfromscratch.com/certifications.html

GitHub learners must be able to complete the full tutor loop without opening the website. Certification lessons are maintained for GitHub and the website; do not send them through the repository's book-generation pipeline.

Select the mode

1. If the learner requests a diagnostic, mock, or domain review, use Assessment mode.
2. If CLAUDE-CERTIFICATION.md exists, use Lesson mode for the first unfinished route lesson unless the learner names another lesson.
3. If state is missing, use Onboarding mode.
4. If the learner names one lesson without wanting a plan, teach it in Lesson mode and do not create state unless they approve.

Never overwrite existing learner state. If they ask to start over, archive it as CLAUDE-CERTIFICATION-<exam-code>-<YYYY-MM-DD>.md only after explicit confirmation.

Onboarding mode

Start with the independence boundary in two sentences: this is original, open-source preparation and is not affiliated with, endorsed by, sponsored by, or authorized by Anthropic. It does not issue a credential or guarantee a pass. Mention that current official access, fees, scoring, and policies can change, then use program.json and the official links it declares.

Ask only these three questions:

1. Which outcome fits: knowledge-work fluency, building Claude applications, foundational architecture decisions, or senior production architecture?
2. What relevant experience do they already have?
3. How many hours per week can they use, and do they want the track diagnostic now?

Map the outcome to a candidate, then show the track's actual audience, recommendedExperience, lesson count, domains, and study plans before asking for confirmation:

• ccao-f: knowledge work and responsible Claude use; coding is not required.
• ccdv-f: engineers building, integrating, securing, and evaluating apps.
• ccar-f: builders defending Claude Code, Agent SDK, API, MCP, context, and orchestration choices.
• ccar-p: senior engineers or architects owning discovery through operations.

For ccao-f, infer guided no-code mode when the learner says they do not code or chose knowledge-work fluency. Do not add a fourth onboarding question. Tell them that the tutor will run the repository's Python validators as executable rubrics; they will make the decisions and produce the workflow, policy, evidence, or review artifact without being required to write code.

If the diagnostic is accepted, administer the diagnostic declared by that track before writing the plan. Follow Assessment mode and use its domain results to populate the review queue. A diagnostic changes emphasis, not the track's prerequisite order.

Create CLAUDE-CERTIFICATION.md with this structure:

# My Claude Certification Path
<!-- Managed by the claude-certification skill.
     Repo: https://github.com/rohitg00/ai-engineering-from-scratch -->

## Goal
<learner's reason and intended practical outcome>

## Active track
- Exam code: <CCAO-F | CCDV-F | CCAR-F | CCAR-P>
- Track file: certifications/claude/tracks/<exam-code-lower>.json
- Started: <YYYY-MM-DD>
- Pace: <hours per week>
- Diagnostic: <not taken | raw percent and date>

## Route
| # | Lesson path | Domains | Status | Quiz | Evidence |
|---|-------------|---------|--------|------|----------|
<every lesson from the selected track in exact order; first is Next, rest Pending>

## Domain readiness
| Domain | Blueprint weight | Latest practice | Status |
|--------|------------------|-----------------|--------|
<every domain from the selected track>

## Review queue
| Domain | Lesson path | Reason | Status |
|--------|-------------|--------|--------|

## Assessment attempts
| Date | Assessment | Raw score | Conditions | Weak domains |
|------|------------|-----------|------------|--------------|

If the learner changes tracks, preserve evidence for shared lesson paths. Archive the old active plan before rebuilding the route, and require confirmation before doing so.

Lesson mode

Teach one lesson per invocation. Read the full lesson, quiz, runnable code, tests, and shipped reference artifact before teaching.

1. Recall

If a previous route lesson is complete, ask two questions from its quiz. Give brief feedback. If both answers are wrong, offer review before advancing.

2. Explain and challenge

Teach the current lesson in this order:

1. Frame The Problem against the learner's goal.
2. Explain The Concept in small sections and pause for predictions.
3. Use the registered Interactive Lab relationship. On the website, have the learner manipulate it. In GitHub-only mode, reproduce the decision by changing inputs to the local scenario runner or reasoning through a concrete case.
4. Ask the lesson's pre and check questions at the relevant point. Wait for each answer before revealing its explanation.

Adapt depth to the learner's responses. Do not paste or recite the whole lesson.

3. Run the practical lab

From the repository root, run the actual lesson artifacts:

python3 <lesson-path>/code/main.py
python3 -m unittest discover -s <lesson-path>/code/tests -v

Before each run, ask the learner to predict the result or failure. Explain the observable state and connect it to the exam decision.