AI-Powered Next.js Development: My Workflow with Claude Code, MCP, and Skills

I've been refining my AI-assisted development workflow for months now. What started as casual ChatGPT prompting has evolved into a structured, repeatable system for shipping Next.js features. Here's the exact workflow I use every day.

The Problem with "Just Prompting"

A common pattern for AI-assisted coding: paste some code, ask a question, copy the answer back. It works for small things, but starts breaking down on larger tasks — multi-file changes across a Next.js app, complex React component trees, or features that touch routing, state, and API layers at once.

The missing piece is usually context. Without enough context, models produce generic or outdated code. My workflow is built around solving that.

Step 1: Build Context in Claude Web

When I pick up a new ticket, I rarely jump straight into code. If the task isn't crystal clear, I start a conversation in Claude's web interface first.

I feed it everything relevant:

• The Jira ticket link with acceptance criteria
• Slack threads with discussions and decisions
• Any existing PRs related to the feature
• Figma design links for the UI specs

This gives Claude the full picture — not just what to build, but why and how it fits into what already exists. It's the difference between "build a modal" and "build a confirmation modal that matches our design system, integrates with the existing notification flow, and handles the edge case discussed in the Slack thread."

Step 2: Generate a Structured Plan for Claude Code

Once the context is clear, I ask Claude to generate a detailed implementation plan specifically formatted for Claude Code. This plan includes:

• Ticket reference — so Claude Code always knows the business context
• Open PRs — to avoid conflicts and understand in-flight changes
• Figma links — Claude Code can inspect designs directly via MCP
• A skills section — tells Claude Code which specialized skills to activate

The skills section defines which tools and constraints Claude Code should use during implementation.

Step 3: Vercel Skills

I use three skills from vercel-labs:

next-best-practice

Checks code against Next.js conventions — proper use of server and client components, correct data fetching patterns, metadata handling, route organization.

vercel-react-best-practice

Covers React-specific patterns — component composition, hook usage, state management, performance considerations.

react-doctor

Analyzes React code for common anti-patterns, performance issues, and potential bugs.

With these skills active, Claude Code produces code that aligns with the patterns my team uses.

Step 4: Context7 for Third-Party Libraries

I use Context7 MCP for third-party library documentation.

AI models are trained on data that includes multiple versions of every library. When you ask Claude to use a library API, it might give you the v2 syntax when you're on v3, or mix patterns from different versions. This comes up often with libraries that have frequent breaking changes — Radix UI, TanStack Query, next-auth.

Context7 provides Claude Code with the actual, current documentation for the version you're using. This reduces hallucinated APIs, deprecated patterns, and version-mismatched code.

Step 5: Automated QA with Agent Browser

The last section I always include in my plan is the expected outcomes for QA. I describe what the feature should look like and how it should behave when it's done.

After Claude Code finishes the implementation, I have it run the agent-browser skill to verify the results. It opens the app, navigates to the relevant pages, and checks that everything matches the expected behavior.

This catches visual regressions, broken interactions, and integration issues before I look at the code myself.

The Full Stack at a Glance

Why This Works

AI coding is a context problem, not an intelligence problem. Models can write solid Next.js code — when they have the right context, constraints, and tools.

My workflow is a context pipeline:

1. Gather context from multiple sources
2. Structure it into a clear plan with skills and tools
3. Execute with guardrails that enforce quality
4. Verify the output automatically

Each step builds on the previous one. Context feeds into plans, plans with skills produce structured code, and automated QA catches issues before review.

If you're using AI for Next.js development and getting inconsistent output, try adding more structure to your workflow. Often the issue is the pipeline, not the model.