Phase 7: CI/CD Pipelines for AI Code · 50 min · GitHub Actions · ESLint · mypy

GitHub Actions for AI Code

Configure every gate as a GitHub Actions step. Cache aggressively. Fail fast.

Real-world application: Engineers who can configure GitHub Actions workflows with quality gates for AI-generated code demonstrate practical CI/CD skills.

What you will learn

Configure GitHub Actions workflows with quality gates for AI-generated code
Implement ESLint, mypy, Bandit, Semgrep, pip-audit, and mutation testing steps
Apply caching strategies for faster AI code verification
Set up required status checks for branch protection

Introduction

GitHub Actions for AI Code

Objective

Run the GitHub Actions workflow simulator with 11 quality gate jobs. Observe the generated YAML, caching strategies, execution times with and without caching, and branch protection configuration. Compare cached vs uncached execution times.

Configure every gate as a GitHub Actions step. Cache aggressively. Fail fast.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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