Name: Claude Code Power User: Architecting AI-Native Dev Workflows
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Created bySchool of AI

Last updated 4/2026

English

What you'll learn

Design and implement a production-grade CLAUDEmd that encodes architecture rules, guardrails, testing policies, and governance standards.
Use Plan Mode to transform ambiguous feature ideas into structured artifacts (REQUIREMENTSmd, ROADMAPmd, STATEmd) with phased execution.
Build and deploy reusable Claude Skills and custom slash commands to automate real-world developer workflows.
Implement mandatory test gating, CI enforcement, and human-in-the-loop workflows for safe AI-assisted development.
Configure Claude Code for large repositories and monorepos using layered governance and folder-level overrides.
Detect and mitigate AI failure modes such as hallucinations, unsafe migrations, and silent logic drift.
Design and document an organizational safe usage policy for enterprise-scale AI adoption.
Deliver a complete capstone repository demonstrating AI-native development practices from planning to production governance.

Course content

8 sections • 65 lectures • 6h 52m total length

Installing Claude Code (CLI/Desktop)8:20
Certificate of Completion0:29
Connecting to Git repositories8:37
How Claude scans and loads your codebase8:12
Context windows and file retrieval strategies8:26
Understanding diffs and edit previews7:42
Core interaction modes (chat, inline edits, planning)8:34
Hands-On Lab: Install and configure Claude Code on a real repo3:05
Hands-On Lab: “TODO → Feature → PR” entirely using Claude Code2:03
Hands-On Lab: Record a walkthrough showing the full implementation lifecycle2:27

When and how CLAUDE.md is loaded8:06
Structuring CLAUDE.md: What / Why / How8:24
Encoding coding standards and testing rules8:20
Enforcing architecture boundaries8:08
Routing Claude to ROADMAP.md and planning artifacts7:52
Align Claud's AI output with long-term goals by routing it to planning artifacts like roadmap.md, requirements.md, and state.md, ensuring deliberate, phased execution and continuity across sessions.
CLAUDE.md patterns for teams vs solo devs8:03
Hands-On Lab: Rewrite CLAUDE.md for an existing API project2:52
Hands-On Lab: Run A/B experiment (with vs without structured CLAUDE.md)2:08
Hands-On Lab: Implement rule-based restrictions (e.g., no direct DB writes)2:11

Using Plan Mode effectively7:53
Multi-phase feature breakdown7:35
Break large AI-native features into clear, incremental phases—design, core implementation, integration, validation, and cleanup—to reduce risk and protect architectural integrity. Define objectives, map dependencies, and execute atomic tasks with guardrails.
REQUIREMENTS.md, ROADMAP.md, PROJECT.md, STATE.md7:43
Atomic task generation and phased execution7:15
Master the execution layer of AI-native development by generating atomic tasks and sequencing them with phased execution, ensuring small, testable, reversible changes and clear outcomes.
Forcing review checkpoints before execution7:27
Enforce structured review checkpoints before execution to preserve architecture, reduce risk, and ensure stability through incremental approvals, diff reviews, and full testing.
Keeping ROADMAP.md as a living artifact7:24
Hands-On Lab: Convert a rough feature idea into structured requirements+roadmap2:22
Hands-On Lab: Run 3 consecutive sessions and track roadmap evolution2:35
Hands-On Lab: Implement checkbox-based tracking with timestamps2:02

What Claude Skills are7:37
Discover what Claude skills are and how they transform repeated workflows into reusable, structured automation. Learn how these modular, persistent execution templates enforce patterns, reduce variability, and scale AI-native engineering.
skill.md structure and YAML headers7:27
Installing community skills6:31
Creating your own reusable skill7:30
Skill orchestration for multi-step flows7:21
Edge-case handling and script integration7:31
Hands-On Lab: Build a “service-scaffold” skill (FastAPI / Spring / Next.js)2:19
Hands-On Lab: Create a “refactor-module” skill with structured constraints2:29
Hands-On Lab: Use a skill-creator skill to generate production-grade skill2:27

Custom slash commands in .claude/commands7:03
Designing flows: /test, /lint, /review, /doc7:20
Integrating with CI and test suites7:31
Align Claude-driven workflows with CI and test suites to prevent drift and ensure local equals CI parity, delivering reliable, production-ready automation through consistent validation and feedback.
Hooks to scripts, CLIs, linters, and external tools7:29
Design deterministic hooks that connect Claude to your toolchain, enabling tests, linters, builds, and security checks while ensuring governance, reliability, and observability.
Guardrails: limiting file scope and diff review enforcement7:29
Guardrails transform AI automation into reliable engineering by enforcing file scope, atomic changes, and structured diff reviews, reducing blast radius and preserving velocity and architectural integrity.
Safe automation patterns for teams8:11
Hands-On Lab: Implement a /test command that runs and summarizes failures2:17
Hands-On Lab: Create a /review command enforcing a code review checklist2:29
Hands-On Lab: Build a /refactor-module command with explicit constraints2:21

Context strategies for large repositories8:16
Avoiding context bloat7:48
Monorepo patterns and folder-level CLAUDE.md overrides8:04
Shared skills across services7:45
Scale automation across services safely with shared skills to reduce duplication and maintain governance. Standardize testing, linting, validation, security scans, and documentation through a service-agnostic layered library.
Claude-assisted PR reviews8:09
Cross-functional usage (PMs, Designers, Data Teams)8:27
Hands-On Lab: Configure Claude for a monorepo environment2:29
Hands-On Lab: Define per-folder instruction overrides2:44
Hands-On Lab: Run dev vs reviewer role-play using Claude in both roles2:03

Failure modes: hallucinations, unsafe migrations, silent logic changes8:04
Designing guardrails in CLAUDE.md8:18
Mandatory test gating7:52
Using STATE.md as an audit trail7:54
Human-in-the-loop workflows8:01
Design structured human-in-the-loop workflows that balance automation speed with safety, ensuring intent, context, governance, and accountability guide AI-assisted development.
Organizational safe usage policy design8:37
Hands-On Lab: Simulate a risky refactor and mitigate it via structured prompts2:32
Hands-On Lab: Implement a “safe usage policy” markdown artifact2:38
Hands-On Lab: Enforce mandatory test runs before code generation2:12

Requirements

Strong proficiency in at least one programming language (e.g., Python, JavaScript/TypeScript, Java, Go, etc.).
Experience working with Git and modern pull request workflows.
Familiarity with backend or full-stack architecture (APIs, services, databases, CI/CD basics).
Understanding of automated testing concepts (unit tests, integration tests, CI pipelines).
Comfort working in a command-line environment and navigating project repositories.
Basic knowledge of software design principles (modularity, separation of concerns, versioning).
Access to a development machine capable of running Claude Code (CLI or Desktop) and a sample repository to practice on.
Recommended (not mandatory): Experience working in a team-based development environment or monorepo setup.

Description

“This course contains the use of artificial intelligence”

Master CLAUDEmd, Skills, Planning Mode, and Automation to Turn Claude Code into Your Project Co-Pilot

AI is changing software development — but most engineers are still using it like autocomplete.

This course is different.

Claude Code Power User is designed for experienced developers who want to move beyond casual prompting and learn how to architect AI-native development workflows. Instead of treating Claude as a chatbot, you’ll learn how to transform it into a structured, governed, and production-safe project co-pilot.

You’ll start by mastering CLAUDEmd — the system contract that encodes architecture boundaries, coding standards, guardrails, and testing policies. You’ll learn how to design it intentionally so Claude operates within your engineering principles instead of improvising.

From there, you’ll dive into Planning Mode and roadmap-driven development. You’ll turn vague feature ideas into structured artifacts like REQUIREMENTSmd, ROADMAPmd, and STATEmd, creating traceable, auditable workflows that connect intent to execution. This is where Claude shifts from code generator to AI-powered project manager.

Next, you’ll build and deploy custom Claude Skills and slash commands to automate real-world engineering tasks — including testing, review workflows, refactoring constraints, and documentation generation. You’ll design reusable automation that scales across teams and services.

But power without governance is risk.

That’s why this course goes deep into mandatory test gating, human-in-the-loop workflows, monorepo context strategies, failure mode mitigation, and organizational safe usage policy design. You’ll learn how to prevent hallucinations, unsafe migrations, silent logic drift, and cross-service overreach.

You’ll explore how to:

Configure Claude Code for large repositories and monorepos
Implement CI-enforced guardrails
Design risk-tier approval workflows
Create enterprise-safe automation policies
Use STATEmd as an audit trail
Perform Claude-assisted PR reviews
Scale AI usage across engineering, product, and data teams

This course culminates in a comprehensive capstone where you embed Claude Code into a real development lifecycle — from idea → roadmap → execution → automation → governance.

By the end, you won’t just “use AI.”

You’ll architect AI-native systems.
You’ll know how to encode discipline into automation.
You’ll know how to scale AI safely across teams.
You’ll know how to turn Claude Code into governed engineering infrastructure.

If you’re a backend engineer, full-stack developer, tech lead, architect, or CTO who wants to design the future of AI-powered development — this course gives you the blueprint.

Claude is not just a tool.

It’s an infrastructure layer.

And this course teaches you how to build on top of it — responsibly, systematically, and at scale.

Who this course is for:

Backend engineers who want to integrate AI deeply into real-world development workflows — beyond autocomplete.
Full-stack developers looking to automate testing, reviews, planning, and refactoring using Claude Code.
ML / AI engineers who want structured governance and production-safe AI development patterns.
Tech leads and engineering managers designing AI-native workflows for their teams.
Software architects responsible for maintaining system integrity while enabling AI-assisted productivity.
DevOps engineers interested in CI gating, automation guardrails, and safe migration patterns.
Startup founders and CTOs building AI-first engineering cultures from day one.
Experienced developers who want to move from “using AI tools” to architecting AI-powered development systems.

What you'll learn

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Course content

Foundations: Claude Code as a Codebase-Aware Agent10 lectures • 58min

CLAUDE.md as a System Contract9 lectures • 56min

Planning Mode & ROADMAP-Driven Development9 lectures • 52min

Skills & Reusable Workflows9 lectures • 51min

Commands, Hooks & Developer Automation9 lectures • 52min

Working at Scale (Large Repos & Teams)9 lectures • 56min

Safety, Governance & Reliability9 lectures • 56min

Capstone: Claude Code as a First-Class Team Member1 lecture • 31min

Requirements

Description

Who this course is for: