Agentic Coding

This article is a comprehensive guide to Claude Code power user workflows that directly addresses your interests. The article covers using parallel worktrees and subagent isolation to run multiple Claude sessions simultaneously, organizing work through plan mode and effort levels, and creating reusable Skills (.claude/skills/) for repeated tasks. It details best practices for maintaining CLAUDE.md files as shared team knowledge bases to prevent repeated mistakes, using the /batch command for large migrations with isolated agents, and implementing verification loops through browser testing and the Chrome extension. The guide includes practical patterns for agentic coding—custom agents for specific workflows like code review, permissions management through pre-approved commands, and hook patterns for automation. Advanced techniques covered include using Opus with extended thinking for complex tasks, leveraging the /simplify command to review code quality in parallel, configuring auto-memory to consolidate learning between sessions, and setting up project-specific Skills and agent definitions in .claude/agents/ directories.

This guide covers structured vibe coding methodology for building SaaS with AI tools like Claude Code and Cursor. It introduces a 3-layer AI Framework built on AGENTS.md core rules, 14 scoped architectural rules, and 22 agent skills with mandatory quality gates. The framework emphasizes PRD-first development, production-ready foundations with auth and payments, and a build workflow that runs through Gate 1 (pre-flight pattern review), Gate 2 (test-driven development), and Gate 3 (typecheck/lint verification). Key practices include using skill-driven workflows to maintain consistency across features, avoiding raw vibe coding's pattern drift by enforcing architectural context, and running a three-phase review cluster (code-review, design-review, qa, security-reviewer) before shipping. The guide shows how the same AGENTS.md and rules work across Claude Code, Cursor, Windsurf, Gemini CLI, and GitHub Copilot, with Claude Code uniquely featuring 10 specialized subagents for planning, reviewing, and QA. It outlines six mistakes that kill AI projects—starting from blank, skipping PRDs, ignoring code review, using unvetted auth solutions, lacking quality gates, and blind refactoring—and provides a production-readiness checklist covering tests, secrets management, migrations, validation, monitoring, and infrastructure-as-code before launch.

Claude Code setups for AI agent development include 1,524 resources across skills, agents, plugins, MCP servers, prompts, hooks, and guides. Key resources include the claude-api skill for building apps with prompt caching and streaming, the mcp-builder skill for authoring custom MCP servers, the ai-engineer agent for designing multi-agent pipelines, and the prompt-engineer agent for tuning production prompts through A/B testing. The directory offers practical examples and concrete templates for implementing these advanced techniques directly in Claude Code.

This course teaches a system-based approach to working with Claude Code and Cursor, covering how to build personal AI workflows over five weeks. The curriculum includes learning to use PRD documents and efficient agent management, with students receiving a pre-built starter workspace template based on the instructor's personal AI system. Key topics focus on advanced usage patterns that go beyond prompt optimization, including how to direct AI work effectively and maintain context across sessions, plus access to an alumni Slack community for ongoing best practices sharing.