Learn Claude Certifications From GitHub

The repository and the website are equal learning surfaces. The website adds interactive figures and browser progress. GitHub gives your AI coding harness the lesson source, scenario code, tests, artifacts, quizzes, diagnostics, and track order it needs to teach you step by step.

Start With an AI Tutor

Clone the repository so the tutor can run every lab and test:

git clone https://github.com/rohitg00/ai-engineering-from-scratch.git
cd ai-engineering-from-scratch

Claude Code discovers the repository tutor automatically. Start with:

/claude-certification

For Codex, Cursor, or another local agent that reads SKILL.md, install the portable course skills:

npx skills add rohitg00/ai-engineering-from-scratch

Then invoke /claude-certification. For ChatGPT or any harness that does not install local skills or support slash commands, attach or open this repository and paste this prompt:

Read skills/claude-certification/SKILL.md completely. Use it to choose my
Claude certification track, create my learning plan, and teach me one lesson
at a time with the real labs, artifacts, quizzes, and remediation in this repo.

The tutor asks about your goal, experience, pace, and whether you want the track diagnostic. It writes CLAUDE-CERTIFICATION.md, then resumes from that file in later sessions. Each lesson requires you to:

1. explain the decision in your own words;
2. predict and manipulate the lesson scenario;
3. run the checked-in lab and tests;
4. build or defend your own artifact;
5. pass the lesson quiz;
6. remediate weak exam domains before advancing.

Your work belongs under learning-artifacts/claude/, separate from the completed reference artifacts in each lesson.

Choose a Route

Track	Best fit	Route	Diagnostic	Full mock
CCAO-F	Knowledge work, analysis, validation, and responsible Claude use	9-lesson route	16 questions	60 questions
CCDV-F	Engineers building and securing Claude applications	15-lesson route	16 questions	53 questions
CCAR-F	Builders defending Claude Code, Agent SDK, API, MCP, and orchestration choices	21-lesson route	15 questions	60 questions
CCAR-P	Senior engineers and architects owning discovery through operations	25-lesson route	14 questions	63 questions

The track JSON is the machine-readable source for route order, prerequisite coverage, domain weights, study plans, and assessment paths. The tutor reads it instead of guessing from a generic study plan.

Use Guided No-Code Mode for Associate

CCAO-F does not require software-development experience. Its lessons still ship Python because a deterministic validator makes the policy, evidence, workflow, and review rubrics testable. The tutor can run that code for you; you are not required to write it.

Paste this after installing or opening the tutor:

Start me on CCAO-F in guided no-code mode. Run the local validators for me,
teach every scenario interactively, and help me create each learner-owned
workflow, policy, evidence, or review artifact from my decisions. Do not skip
the practical work or quizzes, and do not require me to write Python.

You will still predict outcomes, manipulate scenarios, defend choices, revise failed artifacts, and take the original assessments. The interface changes; the evidence standard does not.

Learn One Lesson Manually

Every certification lesson has the same GitHub contract:

certifications/claude/lessons/NN-lesson/
├── docs/en.md          full lesson and interactive-lab reasoning
├── code/main.py        scenario runner, simulator, scorer, or validator
├── code/tests/         deterministic verification
├── outputs/            completed reference artifact
└── quiz.json           six grounded questions with explanations

Open the next lesson path from your selected track. Read docs/en.md, predict the scenario result, then run:

LESSON=certifications/claude/lessons/27-enterprise-governance-compliance-and-hitl
python3 "$LESSON/code/main.py"
python3 -m unittest discover -s "$LESSON/code/tests" -v

Lesson 27 is a governance example: its runnable work validates a policy and human-review packet. It does not add artificial provider code to a conceptual topic. Other lessons ship threat models, ADRs, approval flows, evidence bundles, tool-loop simulators, RAG reports, API lifecycle labs, and capstone verifiers.

Use outputs/ as the completed example. Create your own version in