how to use claude code

About 6,600 people in the US search for “how to use Claude Code” each month. The installation is the easy part. The real skill is deciding what Claude may touch, giving it enough context without flooding the session, and defining what “done” means before it writes anything.

What Claude Code actually does

Claude Code is a working session connected to your development environment. It can read files, search a repository, edit code, run terminal commands, use git, fetch documentation, and inspect errors from compatible code-intelligence plugins. It is available in the terminal, VS Code, JetBrains IDEs, the Claude desktop app, the web, mobile, Slack, and CI/CD workflows.

Think of the language model as the engineer and Claude Code as the workshop around it. The model reasons about the task. The workshop supplies files, tools, permissions, memory, and a place to run checks. That is why it can respond to “fix the failing tests” by reading the failure, finding the relevant code, editing several files, rerunning the tests, and adjusting when the first fix does not hold.

Underneath, every task follows a three-part loop:

1. Gather context: inspect the repository, error output, git state, project instructions, and any references you provide.
2. Take action: edit files, run commands, use tools, or ask you for a decision.
3. Verify: run tests, builds, linters, or another check, then loop if the result fails.

Claude Code can execute locally on your machine, in an Anthropic-managed cloud environment, or through Remote Control, where the code stays on your machine while you operate the session from a browser. The agent loop is the same, but the location of your files, tools, and network access changes.

How to install and start Claude Code

The terminal version gives you the clearest view of how Claude works. You need a code project and either a paid Claude plan, a Claude Console account, or access through a supported cloud provider. The free Claude plan does not include Claude Code.

Anthropic recommends its native installer. Choose the command that matches your terminal:

# macOS, Linux, or WSL
curl -fsSL https://claude.ai/install.sh | bash

# Windows PowerShell
irm https://claude.ai/install.ps1 | iex

# Windows with WinGet
winget install Anthropic.ClaudeCode

Native installs update in the background. Homebrew, WinGet, and Linux package-manager installs require manual updates by default. Run claude --version to verify the installation or claude doctor if the setup looks wrong.

Then open the project you want Claude to understand:

cd /path/to/your-project
claude

The first run opens a browser login flow. After that, start with a read-only question such as “What does this project do, where is the main entry point, and how do I run its tests?” Claude reads files as needed, so you do not have to attach the repository one file at a time.

If you would rather avoid the terminal, install the VS Code extension or open the Code tab in the Claude desktop app. The web version at claude.ai/code runs work in a cloud environment and is useful for long tasks or repositories you do not keep locally.

The workflow that produces reliable work

The strongest default is simple: scope, plan, build, verify, review. Use all five for multi-file work. Skip planning only when the change is obvious enough that you could describe the diff in one sentence.

1. Start with a bounded outcome

“Improve this app” forces Claude to scan broadly and guess what matters. A better request names the symptom, likely location, constraints, and passing condition:

The useful parts are not prompt decoration. Each closes a decision Claude would otherwise make for you.

2. Enter Plan mode before a risky change

Press Shift+Tab until the terminal shows Plan mode, or start with claude --permission-mode plan. Claude may read files and use read-only commands, but it will not edit your source. Ask it to trace the current flow, identify affected files, list risks, and propose verification.

Review the plan like a small technical brief. Correct a wrong assumption now, before it becomes a five-file patch. Approve the plan when the proposed scope and checks make sense.

3. Give it a check it can run

Tests, a build exit code, a linter, a fixture comparison, or a screenshot can all serve as a pass-or-fail signal. Without one, Claude stops when the work looks plausible. With one, it can read the failure and try again.

For a backend change, ask for a failing test first and a green test suite at the end. For a UI change, provide the reference image and ask Claude to compare a fresh screenshot. For a migration, require a count of remaining deprecated calls. Evidence is faster to review than an assurance that the work is complete.

4. Keep permission scope proportional to the task

Default mode asks before edits and most shell commands. Accept Edits mode removes repeated file-edit approvals while still protecting other actions. Plan mode prevents source edits. Auto mode uses background safety checks, but it remains a research preview.

Do not use Bypass Permissions on your everyday machine. Anthropic limits that mode to isolated containers or virtual machines because it skips most prompts. Permissions also do not make a bad instruction safe. Deny rules, sandboxing, and hooks are the stronger controls when a path or action must remain off-limits.

5. Review the evidence, not every keystroke

Ask Claude to summarize changed files, show git diff, report the exact test command and result, and name any unresolved risk. If the work is sound, ask it to create a descriptive commit or pull request. If the direction is wrong, press Esc to stop it. Double-tap Esc or run /rewind to return to an earlier checkpoint.

Checkpoints cover file edits made through Claude's editing tools. They do not reverse a database write, deployment, API call, Bash-driven change, or external process. Keep git in the loop.

Make Claude remember the project, not the whole conversation

A CLAUDE.md file is the durable operating note for a repository. Claude reads it at the start of each session. Run /init to generate a first version, then keep only instructions that Claude cannot reliably infer from the code.

Good entries include the preferred test command, unusual architecture rules, required environment variables, branch conventions, and a known project trap. Do not paste a file-by-file tour or generic programming advice. Anthropic recommends keeping each file under 200 lines because long instruction files consume context and reduce adherence.

Use hooks when a rule must execute every time. “Run the formatter after an edit” belongs in a hook if exceptions are unacceptable. Use skills for procedures that only matter sometimes. Use MCP when Claude needs an external service such as an issue tracker or design system. Use subagents when an investigation would read enough material to crowd the main session. The Claude Code subagents explainer goes deeper on that isolation model.

Context is the scarce resource. The conversation, file contents, command output, instructions, and tool definitions all compete for it. Run /clear between unrelated tasks. Use /compact when the task is still related but the session has grown long. Resume the latest session with claude --continue, or choose an older one with claude --resume.

Eight Claude Code use cases, ranked by practical payoff

1. Ship a bounded feature across several files

A product engineer adding Google OAuth could ask Claude to map the current session flow in Plan mode, name every affected interface, write callback tests, implement the change, run the suite, and prepare the pull request. The payoff is coordinated work across routes, data models, configuration, and tests without manually feeding each file into a chat.

2. Reproduce and repair a production bug

An on-call engineer with a stack trace could give Claude the reproduction command and the suspected module. Claude could run the failure, trace the call path, write a regression test, apply a fix, and rerun the narrow suite. This pays when the expensive part is locating the cause, not typing the patch.

3. Onboard an engineer to an unfamiliar codebase

A new hire could ask for the repository's entry points, data flow, authentication path, deployment commands, and a glossary of internal terms. Follow-up questions can move from broad architecture to a single function. That reduces interruptions for the senior engineer who would otherwise repeat the same tour.

4. Modernize repetitive legacy code

A platform team migrating a deprecated API could have Claude find every use, propose a small batch, preserve compatibility, change the first group, run focused tests, and count what remains. Small verified increments matter here. A one-shot request to “modernize the repository” creates too much room for silent breakage.

5. Pay down test debt

A maintainer could ask Claude to find untested branches in a named service, study the project's existing test style, add cases for the highest-risk paths, and run only the relevant suite. The payoff is not the number of tests. It is a repeatable path from an uncovered behavior to evidence that the new test fails for the right reason and passes after the fix.

6. Prepare a cleaner pull request

Before review, a developer could ask Claude to inspect the current diff, remove accidental changes, run lint and tests, summarize the behavior change, identify risk, and draft a PR description. Human reviewers receive a smaller, better-explained change instead of reconstructing intent from the patch.

7. Keep documentation aligned with code

A developer-relations or platform team could point Claude at a changed API and ask it to update examples, README instructions, and release notes, then verify that every referenced command still runs. People ask AI assistants for “code documentation generator” about 81 times a month, up from 49 a year earlier, even though the exact Google query is much smaller. That gap suggests the job is often expressed inside an AI conversation rather than a search box.

8. Automate a recurring repository chore

An engineering manager could turn a dependency audit, release-note draft, or morning PR triage into a scheduled routine, a desktop task, or a CI command using non-interactive claude -p. The payoff is consistency on work that is important but repeatedly deferred. Unattended tasks need tighter permissions and an explicit output destination because no person is present to answer a vague follow-up.

Three products worth building around Claude Code

The broad coding-assistant market is already crowded. The viable products are narrow control layers that own a costly workflow, preserve evidence, and give a buyer a reason to choose them over opening Claude Code directly.

Strongest: a vertical PR risk gate

Build a reviewer for one expensive failure class, such as authorization mistakes in fintech APIs, unsafe migrations in health software, or breaking changes in a TypeScript platform. It would inspect the pull request against repository-specific policy, run approved checks, and return file-level evidence rather than generic style comments. Engineering leads and compliance-minded teams are the buyers.

The demand is smaller than general coding, but much sharper. “AI code review” receives about 1,300 US searches a month, with a $63.85 CPC. “AI powered code review platform” adds 1,600 monthly searches and has risen 3,173% year over year. Greptile charges $30 per seat per month for its code-review plan, which proves teams already accept a recurring seat price for this job.

The smallest sellable version is one GitHub integration, one language or framework, five high-value checks, a review comment with evidence, and a rerun after the patch. Claude Code's agent loop or the Claude Agent SDK can handle repository inspection and tool execution. Your actual product is the policy pack, evaluation set, and audit trail.

The catch is direct competition from general reviewers and coding platforms. A generic “AI reviewer” has no defensible edge. This is the strongest opportunity only if the product catches domain failures a broad reviewer cannot recognize and can prove that performance on a maintained test set.

A Claude Code rollout kit for teams

Build a guided setup and policy layer for engineering managers adopting Claude Code across several repositories. It would audit installation, generate a concise first CLAUDE.md, propose permission rules, install approved hooks, run a guided repository task, and produce an admin-ready adoption checklist.

The exact how-to query attracts 6,600 US searches a month at a $5.71 CPC, yet the ChatGPT citation dataset currently returns no cited sources for it. A dedicated Claude Code course already charges $50 once, while Claude Pro costs $20 monthly. That combination shows both learning friction and a modest willingness to pay.

An MVP could be a local CLI plus a 60-minute guided workflow for one stack. Sell the first version as an implementation package, then turn repeated checks into software. The catch is that a tutorial alone becomes obsolete as Anthropic improves onboarding. Durable value must come from company policy, repository configuration, and measured adoption, not screenshots of the install flow.

A coding operator for one vertical stack

Build a constrained coding assistant for a repeatable agency job, such as Shopify theme maintenance, WordPress plugin repair, or a specific internal-tool framework. The buyer submits a scoped ticket, reviews a plan, and receives a tested pull request in the patterns that stack expects.

“AI coding assistant” draws about 22,200 US searches a month with commercial intent and a $27.85 CPC. The more specific “agentic AI coding assistant” query draws another 2,900 searches with transactional intent. The market is clearly large, but broad assistants already own the generic use case.

The MVP should support one framework, one repository host, one verification suite, and one approval gate. Build the intake, policy, and evidence layer around Claude Code or the Agent SDK. The catch is platform dependence and liability for bad patches. If the product cannot define a narrow task that it verifies better than a general session, it is only a thinner interface on someone else's agent.

What Claude Code does not solve

Claude Code does not remove the need for engineering judgment. It can produce a patch that passes narrow tests and still violates a product assumption, creates a security problem, or makes the system harder to operate. Your checks define only the failure modes they cover.

Permissions reduce accidental scope, but they do not validate your intent. Checkpoints can restore edited files, but not remote side effects or shell-driven changes. A long session can also become worse and more expensive as context fills. Clear unrelated work, keep project instructions short, and use a fresh reviewer when the change matters.

Cost is another variable. Claude Code is included in paid plans, starting with Pro at $20 monthly or $17 per month with annual prepayment. Usage shares the same pool as other Claude surfaces, and there is no fixed message count. API customers pay by token consumption. If price is the deciding factor, read the current Claude Code pricing breakdown before standardizing a team workflow.

The honest take is simple: use Claude Code for work with a clear boundary and a machine-checkable finish line. Keep architecture decisions, sensitive operations, and ambiguous product tradeoffs under human control. The agent is strongest when it can inspect freely, act within a defined box, and prove what happened.

If you want a repository-aware coding agent or one of these vertical workflows built for your business, see AI agent development.