How to Use Claude Code Skills in Large Codebases: Anthropic's 7-Layer AI Strategy
Anthropic's guide to working in large codebases covers global rules, hooks, skills, LSP, MCP, and sub-agents. Here's the 7-layer AI layer explained.
Why Large Codebases Break Most AI Coding Setups
Most developers hit a wall when they try to use AI coding assistants on anything larger than a side project. The model loses context, ignores repo conventions, makes edits that conflict with existing patterns, or just refuses to go near a 200,000-line monorepo.
Claude Code was built specifically for this problem. And Anthropic’s answer to it isn’t just “a bigger context window” — it’s a structured, seven-layer architecture that lets you build persistent AI workflows across large codebases. Understanding how these layers stack together is what separates a useful Claude Code setup from a frustrating one.
This article breaks down each layer: global rules, hooks, skills, LSP, MCP, sub-agents, and the overarching agent loop that ties them together.
The Core Problem: Context Doesn’t Scale Linearly
In a small codebase, you can paste a file into a chat window and get useful help. In a large one — multiple services, complex CI pipelines, years of accumulated conventions — that approach falls apart fast.
The challenge isn’t just size. It’s structure. A large codebase has:
- Established naming conventions the model doesn’t know about
- Architecture decisions baked into patterns across hundreds of files
- Domain-specific terminology that doesn’t appear in training data
- Tests, linters, and pipelines that enforce specific constraints
- Dependencies between components that aren’t obvious from any single file
Day one: idea. Day one: app.
Not a sprint plan. Not a quarterly OKR. A finished product by end of day.
Cramming all of this into a prompt doesn’t work. Instead, Anthropic’s approach is to build persistent, layered context so Claude Code understands your codebase the way a senior developer would — not just what it can see in a single session.
Layer 1: Global CLAUDE.md Rules
The foundation of the architecture is CLAUDE.md — a Markdown file that Claude Code reads automatically at the start of every session.
At the global level (typically in your home directory or a user-level config), this file holds instructions that apply across all your projects. Think of it as your personal preferences layer:
- Your preferred code style (tabs vs spaces, semicolons vs not)
- Languages you work in most
- How you like tests structured
- Common patterns you prefer (e.g., always use async/await over callbacks)
Global rules are persistent. You write them once, and they apply in every Claude Code session without you having to repeat yourself.
What Belongs in Global CLAUDE.md
Good candidates for the global file include style preferences that are truly universal for you — things you’d apply regardless of which repo you’re in. Avoid putting project-specific context here; that’s what Layer 2 handles.
Layer 2: Project-Level Configuration
Each project can have its own CLAUDE.md file in the root directory, and subdirectories can have their own as well. Claude Code reads these hierarchically, so a file in /src/auth/CLAUDE.md applies specifically when working in that directory.
This is where project-specific context lives:
- Architecture overview (e.g., “This is a microservices app with five services: auth, billing, notifications, search, and core”)
- Naming conventions specific to this repo
- File organization logic
- What to avoid (e.g., “Do not modify files in /legacy — they’re deprecated but still in use”)
- Links to key documentation or design decisions
A well-written project-level CLAUDE.md is genuinely one of the highest-leverage things you can do for AI-assisted development. It means every session starts with Claude Code already knowing what kind of codebase it’s in.
Tips for Writing Effective Project CLAUDE.md Files
- Keep it factual, not instructional. Describe your codebase as it is.
- Include the things a new developer would need to get oriented in the first hour.
- Update it when conventions change — stale context is worse than no context.
- Use subdirectory CLAUDE.md files for modules with their own logic (e.g., a payments module with specific compliance constraints).
Layer 3: Hooks
Hooks are event-driven callbacks that run at specific points in the Claude Code workflow. They give you programmatic control over what happens before and after Claude takes an action.
Anthropic introduced hooks to address a real pain point: you want Claude Code to be autonomous, but you also need guardrails. Hooks let you add those guardrails without manually supervising every action.
Pre-Tool Hooks
Pre-tool hooks run before Claude executes a tool call. Common use cases:
- Block writes to certain directories (e.g., prevent edits to vendor code)
- Validate that a file is writable or not locked
- Log all proposed changes before they happen
- Require human approval for destructive operations
Post-Tool Hooks
Post-tool hooks run after a tool call completes. Common use cases:
- Auto-run linting or formatting after every file edit
- Trigger test runs after changes to core modules
- Send notifications to a Slack channel when certain files are modified
- Update a change log automatically
Remy is new. The platform isn't.
Remy is the latest expression of years of platform work. Not a hastily wrapped LLM.
Hooks are written as shell scripts or executables that receive event data in JSON via stdin. They return structured JSON to tell Claude Code whether to proceed, abort, or surface a message.
This layer transforms Claude Code from a suggestion engine into something that fits your actual CI/CD and code review processes.
Layer 4: Skills
Skills are custom tools you expose to Claude Code — discrete capabilities it can call to accomplish specific tasks.
Out of the box, Claude Code has tools for reading files, writing files, running bash commands, and searching codebases. Skills extend this with anything specific to your workflow.
What Skills Look Like in Practice
A skill is defined with a name, a description (which Claude uses to decide when to invoke it), and input/output schemas. Examples:
- A
create_componentskill that scaffolds a React component with your project’s exact template - A
run_security_scanskill that runs your specific SAST tooling on a file - A
query_database_schemaskill that fetches live schema info from your dev database - A
fetch_design_specskill that pulls the latest Figma spec for a given component
Skills let you capture institutional knowledge as callable tools. Instead of Claude trying to infer how to scaffold a new module, you give it an exact tool that does it right every time.
This is where the architecture starts to feel genuinely powerful for large teams. Skills become a shared library of correct behaviors — not just prompts, but executable, version-controlled, testable capabilities.
Layer 5: LSP Integration
LSP stands for Language Server Protocol — the standard interface that editors like VS Code use to communicate with language servers for features like go-to-definition, find-references, hover documentation, and diagnostics.
Claude Code’s LSP integration means it can query a running language server to get code intelligence at the level of your actual compiler or type checker — not just pattern-matching on text.
Why This Matters for Large Codebases
In a large TypeScript or Python codebase, understanding what a function does requires understanding its type signatures, its call sites, and what it imports. LSP integration gives Claude Code:
- Precise type information — not inferred from variable names, but from the actual type system
- Real-time diagnostics — it can see what errors and warnings exist before making changes
- Accurate symbol resolution — finding the right definition of a function when there are multiple with similar names
- Refactoring precision — renaming a symbol correctly across all references, not just textual matches
Without LSP, Claude Code is essentially reading source code like a text document. With it, it understands your code the way an IDE does. For large codebases where a small change in one file can ripple through dozens of others, this is critical.
Layer 6: MCP Servers
MCP — the Model Context Protocol — is Anthropic’s open standard for connecting AI models to external tools and data sources. Think of it as a unified API layer between Claude and the rest of the world.
MCP servers expose capabilities to Claude Code over a standard interface. Claude can discover what an MCP server offers, understand how to call it, and invoke it when needed — all without the capability being hardcoded into Claude itself.
Seven tools to build an app. Or just Remy.
Editor, preview, AI agents, deploy — all in one tab. Nothing to install.
What You Can Connect via MCP
The range of MCP integrations available is broad:
- Version control systems — query Git history, open PRs, check blame
- Issue trackers — read and update Jira tickets, GitHub issues, Linear cards
- Documentation systems — search Confluence, fetch Notion pages, read internal wikis
- Observability tools — pull logs from Datadog, query error rates from Sentry
- Databases — read schema info, run queries against dev/staging environments
- Communication tools — post to Slack, create calendar events
MCP is what connects Claude Code to the broader context of your engineering organization. A developer working in a large codebase isn’t just editing files — they’re also reading tickets, checking logs, referencing past decisions, and coordinating with others. MCP lets Claude Code participate in that wider context.
MCP vs. Skills
Skills are custom tools you build specifically for your workflow. MCP servers are typically broader integrations with existing systems. In practice, you’ll use both: skills for repo-specific capabilities, MCP for organization-wide systems.
Layer 7: Sub-Agents
The final layer is multi-agent orchestration — Claude Code’s ability to spawn sub-agents that work in parallel on different parts of a task.
This is the layer that makes truly large-scale automated changes feasible.
How Sub-Agents Work
When Claude Code encounters a task that spans multiple components — say, updating an authentication flow that touches the API layer, the database schema, the frontend forms, and the test suite — it can decompose the task and run parallel agents on each part.
Each sub-agent:
- Operates with its own context and tool access
- Can read and write files independently
- Reports results back to the orchestrating agent
- Can spawn further sub-agents if needed
The orchestrating agent coordinates the work, resolves conflicts, and synthesizes the results.
When Sub-Agents Matter
For small changes, you don’t need them. But for:
- Large refactors across many files
- Updating all instances of a deprecated pattern across a monorepo
- Generating comprehensive test coverage for a large module
- Performing a multi-step migration (schema change → data migration → API update → client update)
…sub-agents turn hours of sequential work into something that can run in parallel and complete much faster.
Anthropic’s guidance is to be deliberate about when to use them — they add complexity, and if the orchestration logic isn’t clean, parallel agents can create conflicts. But for the right tasks, they’re a step change in capability.
How the Seven Layers Fit Together
It’s worth stepping back and seeing how these layers interact in a real session.
When you start a Claude Code session in a large codebase:
- Global and project CLAUDE.md load immediately — Claude knows your conventions before you type anything.
- Hooks are registered — any destructive operations will require approval; linting will run after every save.
- Skills are available — Claude knows it can call
create_componentorrun_security_scanwhen needed. - LSP is connected — Claude can query type information and diagnostics as it reads your code.
- MCP servers are accessible — Claude can pull relevant tickets, search docs, or query logs if needed.
- Sub-agents are available if a task warrants parallel execution.
Everyone else built a construction worker.
We built the contractor.
One file at a time.
UI, API, database, deploy.
A single instruction like “Add OAuth2 support to the user authentication flow” can trigger all six layers plus the underlying agent loop — reading config, checking types, pulling API docs via MCP, running tests via hooks, and potentially spawning sub-agents to handle the frontend and backend in parallel.
Where MindStudio Fits: Extending Skills Beyond the IDE
The Skills layer is where Claude Code’s architecture becomes most composable — and it’s where teams often hit a gap. Building every skill from scratch means handling API auth, rate limiting, retries, and integration logic yourself.
MindStudio’s Agent Skills Plugin is an npm package (@mindstudio-ai/agent) that solves exactly this. It exposes 120+ pre-built typed capabilities as simple method calls that any AI agent — including Claude Code — can invoke directly.
Instead of building a custom skill to send emails, search the web, generate images, or run a workflow, you call agent.sendEmail(), agent.searchGoogle(), or agent.runWorkflow() and MindStudio handles the infrastructure underneath.
For teams building out their Skills layer, this cuts the setup work significantly. You can focus on the reasoning logic and task definitions — what Claude Code should do and when — while MindStudio handles the execution layer.
You can also use MindStudio to build the workflows themselves. If you want a Claude Code skill to trigger a broader multi-step process (say, opening a Jira ticket, notifying Slack, and updating a Notion doc when a certain refactor is complete), you can build and host that workflow in MindStudio and call it as a single skill.
Try it at mindstudio.ai.
Frequently Asked Questions
What is Claude Code and how is it different from Claude in a browser?
Claude Code is a command-line AI coding tool built by Anthropic that runs directly in your terminal. It has access to your filesystem, can execute commands, and operates as an autonomous agent within your development environment. The browser-based Claude is a conversational interface — useful for discussion, but it can’t read your files, run your tests, or make changes to your codebase directly.
What is CLAUDE.md and where should it go?
CLAUDE.md is a Markdown file that Claude Code reads automatically to understand your preferences and project context. It can live in your home directory (for global preferences), in your project root (for project-wide context), or in subdirectories (for module-specific context). Claude Code reads these hierarchically — global first, then project root, then the most specific directory for the current file.
What is the Model Context Protocol (MCP) and why does it matter for coding?
MCP is Anthropic’s open standard for connecting AI models to external tools and data sources. For coding, it means Claude Code can reach outside your filesystem to query issue trackers, pull documentation, check observability dashboards, or interact with any system that has an MCP server. It’s what allows an AI coding assistant to operate with the full context of a software engineering organization, not just the files on disk.
How do Claude Code hooks work in practice?
Hooks are scripts that run before or after Claude takes an action. You define them in your Claude Code configuration, and they receive event data (what action was taken, on what file, with what content) via stdin as JSON. They return JSON telling Claude whether to proceed, abort, or show a message. A common setup: a pre-write hook that blocks changes to vendor directories, and a post-write hook that runs your linter automatically.
When should you use sub-agents in Claude Code?
Sub-agents make sense for tasks that genuinely decompose into independent parallel workstreams — large refactors, multi-service changes, comprehensive test generation. For simple edits or focused tasks, the overhead of orchestration isn’t worth it. The rule of thumb: if the task would benefit a human from being split across two developers working simultaneously, it’ll benefit from sub-agents.
Can Claude Code work with any language or framework?
Yes, with some caveats. Claude Code works best with languages that have strong LSP support (TypeScript, Python, Go, Rust, Java are well-covered). The underlying model has broad knowledge of most mainstream languages and frameworks. For less common or domain-specific languages, the project CLAUDE.md and custom skills become more important for supplying context the model wouldn’t have from training.
Key Takeaways
- The seven-layer architecture — global rules, project config, hooks, skills, LSP, MCP, and sub-agents — gives Claude Code a structured way to handle the complexity of large codebases.
- CLAUDE.md files at the global and project level are the highest-leverage starting point; well-written configuration dramatically improves output quality from the first prompt.
- Hooks bring Claude Code into your existing CI/CD and review processes without manual supervision.
- Skills and MCP together allow you to define exactly what Claude Code can do and what external systems it can access — both matter for real-world enterprise workflows.
- LSP integration is what gives Claude code-level understanding rather than text-level pattern matching.
- Sub-agents unlock parallel execution for large-scale tasks, but require deliberate design to avoid coordination conflicts.
If you’re building on top of the Skills layer and want a faster path to production-ready capabilities, MindStudio gives Claude Code and other AI agents access to 120+ typed methods through a single SDK — no infrastructure to manage.
