Claude Code is Anthropic’s AI coding agent, available as a CLI, desktop app, and IDE extension. Out of the box it handles code generation, debugging, and refactoring. With hooks, MCP servers, plugins, skills, and CLAUDE.md configuration, it becomes a customized development tool tailored to your team’s stack and workflows — and increasingly, the substrate teams use to build internal agents on top of.
The posts below explore how to extend and operate Claude Code beyond its defaults — from configuring extension points to composing them into production development workflows that actually improve throughput. Our coverage focuses on what holds up under real load: deterministic guardrails, observable tool calls, and prompts that survive model upgrades.
Topics include hook configuration for pre- and post-action automation, building MCP servers that integrate with your internal tools, writing custom skills for repeatable workflows, structuring CLAUDE.md files for team-wide consistency, and composing these features into development pipelines that reduce friction without sacrificing control.
For a field-tested walkthrough of how engineering teams actually deploy the agent day-to-day — including session hygiene, permission boundaries, and the failure modes nobody warns you about — start with our deep dive on Claude Code in production. If you’re curious about what makes the agent tick under the hood, the anatomy of an AI agent system prompt breaks down the architectural choices Anthropic shipped and what they mean for anyone building on the platform.
Whether you’re a solo developer extending Claude Code with a single hook or an engineering org standardizing CLAUDE.md across dozens of repos, the goal is the same: turn a general-purpose coding agent into a sharp, repeatable tool that fits your stack.