Hands-On Claude Code & Claude Skills: From Zero to Production-Ready AI Workflows

Why This Course?

AI-assisted development is no longer optional — it is the new baseline for engineering velocity. Most developers have used Claude.ai to answer a question or explain code. Very few know how to make Claude a first-class, automated member of their engineering workflow: reviewing PRs without being asked, writing and running tests, enforcing coding standards across a monorepo, or packaging reusable AI capabilities their entire team can install in two minutes.

This course closes that gap. It goes far beyond "prompt Claude in a chat box" and teaches you to wire Claude Code into your terminal, your CI pipeline, and your GitHub Actions — then build, test, evaluate, and ship custom Claude Skills that extend what Claude can do for your specific domain.

Every lesson is hands-on. Every module ends with something deployed and working.

What You'll Build

By the end of this course you will have shipped the following real artifacts:

1. A CLAUDE.md project memory file that gives Claude persistent context about your codebase, coding standards, and hard constraints.

2. A shell script that batch-processes an entire codebase — adding type hints, replacing console.log with structured logging, or enforcing naming conventions — using Claude Code in headless mode.

3. A self-healing test pipeline: Claude Code writes unit tests, runs them, reads the failures, and patches the implementation — all without human intervention.

4. A GitHub Actions workflow that automatically reviews every PR diff, posts a structured review comment with categorised findings (bugs, security, missing tests), and requires zero manual setup per repo.

5. A custom MCP server (Node.js) that exposes a search-docs tool backed by a local documentation index, registered and usable inside Claude Code sessions.

6. A standup-formatter Claude Skill (SKILL.md) with a formal eval set, a passing quality score from the description optimizer, and a packaged .skill file your teammates can install from a README in under 5 minutes.

7. A multi-turn AI chatbot React Artifact that calls the Anthropic API directly, maintains conversation history in state, and handles structured JSON output.

8. A drag-and-drop PDF analyser Artifact that accepts file uploads, converts them to base64, sends them as document blocks to the API, and renders summary, key points, and action items.

9. A production-ready PR assistant: a SKILL.md with three action handlers, a bundled Python changelog script, a GitHub Actions headless pipeline, and a 10-case eval set with >80% pass rate — all packaged and documented for distribution.

Who Should Take This Course?

This course is built for anyone who writes, reviews, designs, or ships software professionally and wants Claude working as an automated, reliable team member — not just a chat assistant.

• Software Engineers and Developers who want to automate repetitive workflow tasks: code review, test writing, documentation, and style enforcement.

• System Engineers and DevOps/SRE Engineers who want Claude Code running in CI/CD pipelines, reviewing diffs, and enforcing standards.

• Software Architects and Tech Leads who want to package reusable AI capabilities as Claude Skills and distribute them across teams.

• Product Managers and Engineering Managers who want to understand capability and cost tradeoffs of Claude Code vs API usage.

• QA Engineers who want automated test generation, coverage gap detection, and self-healing pipelines.

• Data Engineers and Technical Writers building AI-powered interactive artifacts.

• UI/UX Designers prototyping AI-powered interfaces using the Anthropic API.

• IT consultants and technical writers entering AI-augmented workflows.

Fresh CS/CE graduates are welcome. Only prerequisites are terminal comfort and basic coding experience.

What Makes This Course Different?

1. Every lesson has running code and a clear expected output — not theory-only.

2. Claude Code (Modules 1–4) and Claude Skills (Modules 5–8) are treated as distinct systems with real integration.

3. The Skill evaluation loop is real: evals.json, run_loop.py, and measurable trigger accuracy.

4. Honest coverage of cost and authentication models (Pro vs API keys, real tradeoffs).

5. The capstone ships a real, production-ready tool with onboarding documentation.

Key Topics Covered

Claude Code Fundamentals

• Installation, authentication (Pro vs API key), and when each applies

• Interactive session management: /add, /remove, /status, @file, glob patterns

• CLAUDE.md project memory design and constraints

• Headless mode with --print and -p

• Piping shell data (git diff, logs, tests) via stdin

• Bash automation scripts for large-scale refactors

• Self-healing pipelines (generate → run → fix loop)

• GitHub Actions CI integration for PR review

• MCP protocol, config, CLI usage, and tool schemas

• Official MCP servers (filesystem, GitHub, inspector)

• Building custom MCP servers in Node.js

Claude Skills Fundamentals

• Skill anatomy: YAML + Markdown + resources

• Writing high-performance description fields (trigger optimization)

• Progressive disclosure with references/

• Skill folder structure (scripts/, references/, assets/)

• Skill intent documentation

• Manual testing strategies and edge case handling

• Iteration using skill-creator

• Bundled scripts and execution patterns

• Multi-domain Skills with routing logic

• evals.json structure and evaluation strategies

• Description optimizer (run_loop.py) workflow

• Packaging and distributing .skill files

Anthropic API in Artifacts

• Calling /v1/messages from React

• Structured JSON prompting and UI rendering

• Multi-turn conversation state management

• MCP integration in API calls

• PDF upload, base64 handling, and structured output

Prerequisites

Required

• Terminal/CLI familiarity

• Basic coding experience (Python or JavaScript preferred)

• Claude.ai account (Pro recommended)

• Node.js 18+

Helpful (Optional)

• Git & GitHub familiarity

• Basic API/HTTP understanding

• Intro-level React exposure

Not Required

• Prior Claude Code / Skills experience

• AI/ML background

• CI/CD experience

Course Structure

The course includes 9 modules and ~30 hours of hands-on work.

Part 1 — Claude Code (Modules 1–4, ~14 hours)

Covers installation → automation → MCP tools → CI pipelines.

Part 2 — Claude Skills (Modules 5–8, ~14 hours)

Covers Skill creation → testing → evaluation → distribution.

Capstone (Module C, ~2 hours)

Build a full PR assistant combining:

• Claude Skill

• GitHub Actions pipeline

• MCP integration

• Evaluation gate

Lesson Structure

Each lesson includes:

• Concept — core understanding

• Task — hands-on objective

• Code — working implementation

• Expected Output — clear success criteria

Curriculum

MODULE 1 — Environment Setup & First Session (3 hours)

• 1.1 Install Claude Code

• 1.2 First interactive session

• 1.3 AI-assisted code edit

• 1.4 Write CLAUDE.md

• 1.5 VS Code integration

MODULE 2 — Core Commands & File Context (3 hours)

• 2.1 Context management commands

• 2.2 File mentions

• 2.3 Glob patterns

• 2.4 Multi-file refactoring

• 2.5 Permission modes

MODULE 3 — Agentic & Headless Workflows (4 hours)

• 3.1 --print automation

• 3.2 Shell piping

• 3.3 Batch scripting

• 3.4 Self-healing pipelines

• 3.5 GitHub Actions integration

MODULE 4 — MCP Servers & Custom Tools (4 hours)

• 4.1 MCP config

• 4.2 Filesystem server

• 4.3 GitHub server

• 4.4 Custom MCP server

• 4.5 Debugging tools

MODULE 5 — Anatomy of a Claude Skill (3 hours)

• 5.1 SKILL.md breakdown

• 5.2 Description optimization

• 5.3 Progressive disclosure

• 5.4 Folder structure

• 5.5 Packaging & install

MODULE 6 — Writing & Testing Your First Skill (4 hours)

• 6.1 Intent doc

• 6.2 Full SKILL.md

• 6.3 Test cases

• 6.4 Manual testing loop

• 6.5 Iteration workflow

MODULE 7 — Advanced Skill Patterns (4 hours)

• 7.1 Bundled scripts

• 7.2 Multi-domain Skills

• 7.3 evals.json

• 7.4 Optimizer

• 7.5 Distribution

MODULE 8 — Anthropic API in Artifacts (3 hours)

• 8.1 First API call

• 8.2 JSON output

• 8.3 Multi-turn state

• 8.4 MCP in API

• 8.5 PDF analyser

MODULE C — CAPSTONE (2 hours)

• C.1 Architecture design

• C.2 Build SKILL.md + handlers

• C.3 GitHub Actions pipeline

• C.4 Eval + optimizer (>80% pass)

• C.5 Package and onboarding README