Claude Code & Full-Stack Development Workflow

Hello! Welcome to the next stage of your journey into advanced Claude Code.

In our last module, we focused on integrating Claude into CI/CD pipelines and, crucially, implementing the guardrails needed to do so safely. We covered tool restrictions, timeouts, and cost management—all essential for production-grade automation.

Now, we shift from individual features and safety to a holistic view. This lesson addresses a core challenge: orchestrating all of Claude's advanced capabilities into a coherent, end-to-end development workflow. Your goal is to map advanced Claude Code capabilities (slash commands, hooks, MCP servers) to each stage of a full-stack feature development process. We will build a mental model of how these tools work in concert, moving Claude from a simple coding assistant to an automated development system.

The Toolkit for an AI-Powered Workflow

Before we map the tools to the process, let's quickly review the "power-user" features at our disposal. Think of these as the fundamental building blocks of any sophisticated Claude Code workflow.

Our task is to strategically apply these mechanisms across the standard phases of software development:

1. Analysis & Planning
2. Implementation (Coding)
3. Validation (Testing & Review)
4. Deployment & Integration

Let's explore how each phase can be supercharged with Claude Code.

Phase 1: Analysis & Planning

The goal of this phase is to transform a high-level feature request into a detailed, actionable plan. This is where you transition from being the "coder" to the "engineering manager," providing clear specifications for your AI counterpart.

Here’s how advanced Claude Code capabilities facilitate this:

• Kickoff with Slash Commands: Instead of a vague prompt, you start with a purpose-built command like /plan or /go. This command can instruct Claude to use a specific planning methodology. The "Claude Code Full-Stack Configuration Guide" highlights this as a best practice.
• Specialized Planning Agents: A powerful pattern is to delegate planning to a sub-agent with read-only permissions. This "Planner Agent" can be instructed to:
  • Use the Sequential Thinking MCP to break down the problem into logical steps.
  • Use a semantic search MCP like Serena to analyze your existing codebase for relevant patterns and dependencies. This is perfect for a full-stack task where you need to understand connections between your FastAPI backend and React frontend.
  • Consult CLAUDE.md for high-level architectural rules.
• The Output: The result of this phase isn't code; it's a structured plan, often in Markdown. This plan might detail the necessary database schema changes, the new FastAPI endpoints to create, and the React components to build. You review and approve this plan before any code is written.

Phase 2: Implementation (Coding)

With an approved plan, the "Developer Agent" (which can be the main Claude session or another sub-agent) takes over. Its sole focus is to execute the plan.

• Project-Wide Consistency with CLAUDE.md: This file is Claude's permanent memory for your project. It contains the rules for your tech stack (e.g., "Always use Pydantic for API models," "Use TanStack Query for data fetching in React"). Since you have deep experience with these tools, you can codify your own best practices here.
• Just-in-Time Knowledge with MCPs and Skills:
  • Context7 MCP: Prevents Claude from using outdated library APIs by fetching real-time documentation for your specific versions of FastAPI, Pydantic, or React.
  • Skills: These are auto-loading context snippets. You could have a skill that provides the PostgreSQL schema summary whenever Claude is about to write a database query with SQLAlchemy.
• Automated Housekeeping with Hooks: A PostToolUse hook can automatically run a code formatter like black or prettier every time Claude saves a file. This ensures all generated code adheres to your style guide without you or Claude having to think about it.

Phase 3: Validation (Testing & Review)

Once the code is written, it must be verified. A robust test suite is the cornerstone of safe AI-assisted development.

• Automated Test Execution: You can create a /test slash command that runs your entire test suite (pytest for the backend, vitest or jest for the frontend). Claude can execute this command and analyze the output to find and fix failures.
• End-to-End Browser Testing: For UI changes, the Playwright MCP allows Claude to control a browser, navigate your React application, interact with forms, and verify that the UI behaves as expected. This provides a powerful feedback loop for front-end development.
• Automated Code Reviews: You can have a /review command that invokes a "Reviewer Agent." This agent, given a persona (e.g., "a senior security engineer"), can audit the code changes against best practices like the OWASP Top 10.
• Quality Gates with Hooks: PreToolUse hooks can act as powerful quality gates. For example, a hook can block a git commit or git push if the test suite fails, ensuring broken code never makes it into your repository.

Phase 4: Deployment & Integration

The final phase involves committing the code and integrating it into the main branch.

• Version Control Automation: A /commit slash command can be created to review staged changes and generate a conventional commit message. Integration with the GitHub MCP or the gh CLI allows Claude to then open a pull request automatically.
• Branch Protection with Hooks: As we've seen, hooks can enforce workflow rules. A check-branch.sh hook can prevent direct commits to main, forcing all changes through a PR, which is a standard best practice.
• CI/CD Integration: Once a PR is created, it can trigger the GitHub Actions workflows we configured in the previous module, running tests and guardrails in an automated, server-side environment.

A Synthesized End-to-End Workflow

Let's walk through a complete example, tying all the phases together. Imagine you want to add a new feature: a user profile page in your React/FastAPI application.

1. Kickoff: You create a GitHub issue and then, in Claude Code, you run:
   /plan "Implement user profile page from issue #42"

2. Analysis: The Planner Agent activates. It uses the GitHub MCP to read issue #42. It uses Serena to find your existing user model and authentication routes. It uses Sequential Thinking to draft a plan:

   • DB: Add bio (TEXT) to users table.
   • API: Create PUT /api/v1/user/profile endpoint in FastAPI.
   • UI: Create /profile route and ProfilePage.tsx component in React.
   • TEST: Add pytest for the API and a Playwright test for the UI.
     You review and approve this plan.

3. Implementation: The Developer Agent takes over.

   • It creates an Alembic migration for the database change.
   • It writes the FastAPI endpoint, using Context7 to ensure it uses the latest Pydantic syntax.
   • It writes the ProfilePage.tsx component, adhering to rules in CLAUDE.md.
   • As it saves files, a PostToolUse hook automatically runs black and prettier.

4. Validation: You instruct Claude:
   "Looks good. Now, validate your work."

   • Claude runs the /test command. pytest passes, but the Playwright test fails because a button's ID is wrong.
   • Claude analyzes the Playwright failure log, corrects the button ID in ProfilePage.tsx, and re-runs the test, which now passes.
   • You then run /review, and the Reviewer Agent confirms that the new API endpoint correctly checks for authentication.

5. Integration: You're ready to commit.

   • You run /commit. Claude generates a commit message: feat(profile): add user profile page and api.
   • The check-branch.sh hook confirms you're not on main and allows the commit.
   • Claude then uses the gh CLI to push the branch and open a pull request, summarizing the changes.

This entire sequence was orchestrated through a combination of commands, hooks, agents, and MCPs, with you acting as the reviewer and approver at key checkpoints.

To see what a fully-realized system of this kind looks like, you can explore the "Claude Code Mastery Project Starter Kit". It's a template that comes pre-packaged with dozens of commands, hooks, and agents designed for this kind of phased workflow.

Conclusion

You have now built a comprehensive mental model for orchestrating Claude Code's advanced features across a full-stack development lifecycle. This represents a significant shift from using an AI for isolated tasks to designing an automated system where the AI executes a structured process under your direction.

Key Takeaways:

• Divide and Conquer: A phased approach (Analysis, Implementation, Validation, Integration) allows you to apply the right tools at the right time.
• Specialize with Sub-agents: Use read-only "Planner" agents for analysis and "Developer" agents with write access for coding to improve reliability.
• Enforce Rules with Hooks: Use hooks for deterministic guardrails and quality gates (e.g., blocking commits, auto-formatting), which is more reliable than prompt instructions.
• Extend Capabilities with MCPs: Integrate external tools like documentation search (Context7), code search (Serena), and browser automation (Playwright) to give Claude the context and abilities it needs.
• Streamline with Slash Commands: Encapsulate complex, multi-step prompts and workflows into simple, reusable commands.

Next Up:

Theory is essential, but practice is where mastery is forged. In our next lesson, we will move from this conceptual map to practical design. You will design a complex workflow where a custom slash command triggers a process involving a hook and a custom MCP server, tailored to a problem in your own FastAPI and PostgreSQL environment. Get ready to architect your first integrated AI automation