AI Coding

Everything Claude Code: What You Should Know

A complete orientation to Claude Code: what it is, how to install it, skills, plugins, subagents, and how the pieces fit into a real developer workflow.

Overview illustration of Claude Code components: terminal agent, skills, plugins, and subagents on a developer desk

Claude Code is Anthropic’s terminal-native AI coding agent, plus the layers around it: skills for reusable instructions, plugins for distributing those, and subagents for parallel work. The system lets you delegate multi-step tasks from your shell, review the results as diffs, and standardize how your team works with AI.

This is a fast-moving toolchain. As of May 2026, the components below behave roughly as described, but model versions, feature names, and defaults change week to week. Use this as orientation and verify every specific against the official Claude Code docs before depending on it.

What is Claude Code at its core?

At its core, Claude Code is an agent that runs in your terminal and drives a repository. You install a CLI, point it at a project, describe a task, and it reads files, edits them, runs commands, and iterates until it reaches a result you review. It is not a chat panel grafted onto an editor, it lives in your shell and operates alongside whatever editor you already use.

The model behind it is from Anthropic’s Claude family, adapted for agentic coding. What makes it an agent rather than an assistant is the loop: it observes your repo, acts on it, observes the result, and decides what to do next. That is the foundation everything else builds on. We unpack the AI itself in more depth in Claude Code AI: What You Should Know.

How do you get started?

Getting started is a four-beat sequence, and most friction lives in authentication and permissions rather than the install itself.

The short version:

Confirm prerequisites, a supported OS, the runtime the docs specify, and an Anthropic account with Claude Code access.
Install the CLI using the current command for your platform.
Authenticate on first run through the browser login flow.
Launch it inside a repo and give it a small, verifiable first task.

The single most important early decision is not technical, it is scoping what the agent is allowed to do on your machine before you set it loose. The full walkthrough, including the common gotchas, lives in Install Claude Code: What You Should Know. Resist pointing a brand-new agent at your most important repo; build trust on a scratch project first.

What are skills, plugins, and subagents?

These three are the extensibility layers, and keeping them straight saves confusion. Each operates at a different scope.

Skills are reusable instruction bundles that load on demand. You write one for a repeated, opinionated workflow, your commit convention, your release checklist, and the agent pulls it in when relevant. They are the “how we do this” layer. More in Claude Code Skills.
Plugins are distribution units. A plugin packages skills, commands, and integrations so a whole team can install the same workflow at once. Skills are the ingredient; plugins are the cookbook you hand around. More in Claude Code Plugins.
Subagents let the main agent delegate a slice of work to a separate agent context, useful for parallel or isolated tasks so one job does not pollute another’s context.

The mental model: the core agent does the work, skills shape how it does a kind of work, plugins distribute those shapes across people, and subagents parallelize the work when one context is not enough.

Lab Notes, adopt the layers in order, not all at once. Most teams need the core agent first, a few skills second, and only later reach for plugins or subagents. Reaching for the advanced layers before you understand the basics adds ceremony before it solves a problem.

How does Claude Code fit a real workflow?

The honest answer is that it fits the workflow of developers who already live in a terminal and prefer to delegate bounded tasks rather than co-edit every line. If your day is a build watcher on one pane and a log tail on another, the agent slots in naturally as a third pane that does work.

A typical loop looks like this. You describe a task at a higher level than a single edit, “migrate this module, run the tests, fix the failures.” The agent goes off, does it, and comes back with a diff. You review at the diff level, not the keystroke level. You accept, refine the prompt, or fix by hand. The whole rhythm assumes you have a strong build, test, and lint pipeline the agent can lean on, because that pipeline is what lets the agent self-correct.

This is a genuinely different posture from an editor-native tool, where you watch each change appear inline. Neither is better in the abstract, they fit different developers and different tasks. We lay out that choice in detail in Claude Code vs Cursor: Choosing Your AI Coding Tool.

What should you watch out for?

Every powerful tool has sharp edges, and naming them is more useful than pretending they are not there.

Keep these in view:

Permissions are real. If you grant shell access, the agent can run destructive commands. Scope what it may do, and read before approving anything irreversible.
Secrets are visible. Anything in the repo the agent can read, it has read. Audit before pointing it at sensitive code.
Confident wrong answers. When a task depends on context the model cannot see, it invents plausible code. Verification, tests, diff review, is your safety net, not optional.
Fast-moving features. Skills, plugins, and subagents are new layers that change between releases. The Anthropic documentation is the source of truth; blog posts age fast here.

The pragmatic stance on the whole system is the same as for any AI tool. It is a power tool that rewards developers who know what good code looks like and who keep verification tight. Use it to compress time on bounded, checkable work. Keep architecture judgment and irreversible changes under your own hand. Tradeoffs first. The tool comes after.

How does Claude Code compare to other AI coding tools?

It helps to place Claude Code on the map rather than evaluating it in isolation, because the “which tool is best” framing is mostly a category error. Most AI coding tools differ less in raw intelligence, they often share model families, and more in where the AI lives and how much you watch it work.

Three rough shapes cover most of the field. Editor-native tools weave AI into a code editor, so suggestions appear inline and you review each change as it happens. IDE plugins bolt onto an editor you already use without replacing it, keeping a light footprint. Terminal agents, the category Claude Code belongs to, run in your shell and drive the repo, so you brief a task and review the diff afterward rather than watching every keystroke.

None of these is universally better. They fit different developers and different work. If your day is inside an editor doing surgical edits, an editor-native tool or plugin will feel natural. If your day is in a terminal with a build watcher running, a terminal agent slots in cleanly. Many working developers run more than one, using each for the workflow it fits.

The honest way to choose is to trial the same real task in each tool, in your own repo, and see which ergonomics match how you already think. Toy demos flatter every tool equally. We walk through that exact comparison between a terminal agent and an editor-native tool in Claude Code vs Cursor, and the broader decision framework in our AI coding assistant overview. Pick by surface and workflow fit, not by hype.

Claude Code AI: What You Should Know, the model and agent loop at the core.
Install Claude Code: What You Should Know, the setup walkthrough.
Claude Code Skills and Claude Code Plugins, the extensibility layers.

Sources

“Claude Code documentation”, Anthropic, official documentation covering the agent, skills, plugins, and configuration.
“Claude Code”, Anthropic, official product overview.