How Claude Code Works in Large Codebases: Best Practices and Starting Points

Claude Code reads live codebases by traversing the file system directly on the developer's machine, using grep and reference tracking, without uploading a central index.

Performance depends more on the harness than on the model. The core components are CLAUDE.md, hooks, skills, plugins, and MCP servers; skills are loaded on-demand, and plugins bundle configurations for distribution.

For large repositories, the following patterns are recommended.

• Keep the root CLAUDE.md thin, and separate local rules into subdirectory files.
• Start from the actual working directory rather than the repository root, so only relevant context is read first.
• Scope test and lint commands per directory as well, and filter out generated files, build artifacts, and third-party code with exclusion rules.
• If the directory structure is complex, maintain a root Markdown map; if it's simple, use @ mentions to specify only the needed files.

When precise navigation is needed, use LSP integration to track definitions and references instead of string search, and connect internal tools and data via MCP servers. Reading and editing can be separated using subagents, splitting parallel exploration from editing.

As adoption grows, governance becomes important. Organizations should define the approved scope of skills and plugins, code review procedures, and permission policies, and designate a DRI or agent manager to manage and update the configuration. It's also recommended to review the harness every 3-6 months, since as the model advances, rules meant for past limitations can become constraints instead.