Claude Code: How to Use an AI Agent for Coding in Russia

Claude Code is a CLI tool from Anthropic for working with code through the terminal. To use Claude Code safely, open a local project in a separate Git branch, start by analyzing files and making a plan, then review changes using diffs, tests, and manual code review.

You need to fix a bug, update a dependency, or add tests in a repository, but analyzing the project structure takes longer than making the change itself. Claude Code helps delegate some of the routine work to an agent through the terminal: it examines files, suggests a plan, and can make changes in the working directory.

It is neither a replacement for a developer nor a chat that produces a code snippet unrelated to the product. The AI agent gets the project context within the permissions available to it, so the quality of the result depends on how the task is specified, the state of the repository, and human oversight.

If a task requires a paid model—for example, Claude Opus 5—it costs less to get access through the Clodex partner service than directly from the vendor. The price difference is shown below.

Key points

• Claude Code works in the terminal and uses the files, directories, and rules of a specific project as context.
• The tool differs from Claude.ai: the web chat sees only the conversation context provided to it, while the CLI works in the selected working directory.
• To get started, you need a local copy of the repository, Git, an available development environment, and an authorization method supported by Anthropic.
• For a safer start, analyze the project structure, explain the code, plan a refactor, or prepare tests without making widespread file changes.
• Before merging changes into the main branch, the developer checks the diff, runs tests, and conducts a code review.
• Check the latest service rules for Claude Code availability in Russia, subscription terms, API access, and free modes.

The first work cycle is straightforward: install the tool, open the project, specify a limited task, ask for a plan, review the changes, and test the result. The clearer the task boundaries, the lower the risk of affecting unrelated modules.

What Claude Code Is and How It Works

Claude Code is a CLI tool, not a regular chat

CLI stands for command-line interface. The developer launches Claude Code from the terminal in the repository’s root folder and describes the task in plain text. The agent can read accessible files, analyze the directory structure, explain module logic, suggest changes, and run commands within the permitted environment.

The work does not have to be automated. Depending on the settings, access permissions, and type of action, Claude Code may show a plan first, ask for confirmation, or limit itself to recommendations. This mode is convenient for the first session: the agent examines the project, and the developer decides which suggestions to apply.

How Claude Code Differs from Claude.ai, the API, and VS Code

The features, limits, and terms of specific Anthropic products change. Check the service’s latest documentation before setting up a workflow, and do not apply subscription rules to the API or vice versa: these are different ways of accessing the model.

What You Can Do with Claude Code

Claude Code is best used for tasks with a clear goal and a verifiable outcome. The agent is useful when you need to quickly gather context, prepare a draft of changes, or go through repetitive development steps, but the team makes the final decision.

Get to know an unfamiliar project

Start by asking for an overview of the repository: entry points, modules, libraries used, how to run it, and test configuration. The agent can organize the files into a clear outline and point out where to find the relevant business logic. Verify its findings against the source code, README, and internal documentation, especially if the project has been developed by several teams over a long period.

This use case is especially helpful before fixing a bug. Rather than immediately trying to change the code, the developer first gets a map of dependencies, identifies module owners, and understands which tests the change will affect.

Fix a local bug

For debugging, provide the symptom, error message, steps to reproduce, and path to the suspected module. Ask the agent to first list likely causes and make a repair plan. This makes it easier to check whether it has mistaken a side effect for the cause or touched code unrelated to the issue.

The question “what happens if you simply write ‘fix the code’?” has a simple answer: the agent will have to guess the context, expected behavior, and scope of the changes. The less information provided, the greater the chance of getting a patch that works on the surface but is wrong.

Prepare tests and documentation

Claude Code can draft unit tests, comments for complex logic, API documentation, or a README update after a feature change. This speeds up routine work when the developer already understands the expected behavior and can assess the set of scenarios.

Tests should not be accepted automatically. Check whether they cover errors, edge cases, exception handling, and critical user scenarios. A test that merely repeats the implementation does not prove that the result is correct.

Carry out a limited refactor

The agent is suited to renaming entities, breaking up a long function, extracting repeated logic, and bringing modules in line with project conventions. Major architectural changes require a separate plan, discussion of implications, and division into small pull requests.

Keep refactoring limited to a specific module or a related group of files. If a task affects a public API, migrations, access permissions, or financial logic, the developer should separately check backward compatibility and rollback scenarios.

Automate routine operations

Claude Code can help prepare a script, configuration, migration, or analyze build errors. Before running a command in a working environment, read what it does and assess the consequences: operations involving files, dependencies, and databases require the same oversight as commands written by a person.

For tasks related to generative models in promotion and technical optimization, SEO Mind42 has materials in the category on AI tools. They should be treated as a source of ideas for workflows, not as a substitute for checking the result.

How to Get Started with Claude Code: Installation and First Launch

Installation commands, system requirements, supported operating systems, and Anthropic authorization methods may change. Before launching, check the instructions for the specific installed version of the tool and your environment: Windows, macOS, or Linux.

Prepare your working environment

Before installing, check not only your terminal but also the state of the project. The local copy of the repository should run without an AI agent, and tests and builds should be available for verification. Otherwise, it will be difficult to distinguish a new error from an existing environment issue.

• Install and check the terminal you use for local development.
• Clone or open a local copy of the project, not the only copy of your working files.
• Configure Git and make sure the change history is available.
• Check the current branch and save any uncommitted changes before starting.
• Remove secrets, .env files, database dumps, and confidential exports from the context.
• Prepare a separate branch or isolated copy of the project for experimentation.

Open the project in the terminal

1. Go to the repository’s root folder. The agent will have an easier time understanding the project structure and finding configuration files.
2. Check the Git status. Commit or temporarily set aside your current changes so they are not mixed with changes made by the AI agent.
3. Launch Claude Code. Use the launch method specified in the built-in help and the latest documentation for the installed version.
4. Complete authorization. Choose only an access option supported by the service, and do not include credentials in the task text.
5. Start with analysis. The first task should explain the project or produce a plan, not change dozens of files.

First task: a safe prompt template

A suitable prompt for the first session:

Study the project structure and explain where authorization is implemented. Do not modify any files. First list the related modules, then suggest a plan for adding a session expiration check.

Once the structure is clear, you can move on to a limited fix:

Find the cause of the specified test failure. First describe your hypothesis and show which files need to be changed. After confirmation, make the smallest possible changes and suggest a command for checking the result.

This sequence keeps the task within bounds: analysis first, then a plan, changes after confirmation, and testing only after that. It is more useful than trying to get the agent to rework the entire subsystem in the first prompt.

How to Give Claude Code Tasks

Specify the goal, constraints, and completion criteria

A good prompt describes five things: what needs to be changed, where to look for context, what must not be changed, what outcome is expected, and how to verify it. The agent does not have to guess the product rules if the developer specifies them in the task from the outset.

Add input validation to the registration handler. Do not change the public API or response format. Add tests for an empty field and an invalid email address. Explain the plan before making changes.

This format sets an API constraint, names the component, defines the expected tests, and requires a plan before editing. If the project has its own conventions, store them in the repository documentation instead of repeating them in every message.

Break a large task into stages

1. Analyze the implementation. The agent identifies related files, dependencies, and existing tests.
2. Plan the changes. The developer checks whether the approach fits the project architecture.
3. Change one component. A limited scope makes review and rollback easier.
4. Review the diff. Git shows the added, removed, and changed lines.
5. Run tests. Verification confirms that the changes have not broken expected behavior.
6. Manual review. The developer assesses readability, security, and whether the changes meet the task requirements.

The broader the task, the higher the risk of affecting unrelated parts of the project. Be especially cautious with requests that change business logic, the database schema, deployment configuration, and the interface all at once.

Do not just say “fix the code”

The minimum context includes the error message, module path, expected behavior, steps to reproduce, and constraints. If the error occurs only in a specific environment, say so explicitly, but do not include credentials, tokens, or confidential event logs in the prompt.

Shared review rules are useful for team development. They help separate the correctness of the logic from code formatting, the size of changes, and dependency security. Approaches to using neural networks in Russian work processes also require an assessment of the legal and organizational restrictions discussed in more detail in the material on the lawful use of neural networks in Russia.

If you decide to take out a paid plan while reading, compare the official price with the price through a partner before subscribing directly: the difference is usually several times, and the calculation is provided at the beginning and end of the article.

How to Use Claude Code in VS Code

VS Code remains the editor and development environment. Claude Code should not be called a VS Code plugin when referring to running the CLI in the editor’s integrated terminal. Open the repository folder in VS Code, go to the terminal, and launch the installed tool from the project root.

1. Open the repository in VS Code. The working folder should match the directory where you plan to run the agent.
2. Create a separate Git branch. It will separate experimental changes from the main development work.
3. Open the integrated terminal. Claude Code, tests, linters, and the build are run there.
4. Ask it to create a plan. Do not proceed to large-scale editing before reviewing the approach.
5. Review the changes in Source Control. The version control panel will show the diff for each affected file.
6. Check the result manually. Run the tests and evaluate the application’s behavior.

The status of third-party extensions, plugins, and MCP connections should be checked separately. The presence of an extension in the editor’s marketplace does not confirm its official status with Anthropic and does not mean that it is safe for a corporate repository.

Commands and settings that may be useful after the first launch

Help and session management

CLI tools usually provide built-in help, launch parameters, and tools for managing the current session. Command syntax changes along with versions, so you should not copy random instructions from old publications. Open the help for the installed tool and compare it with Anthropic’s documentation.

Check separately which actions the agent offers to perform itself and which require confirmation. Permission settings are best viewed as part of the security model rather than as an obstacle to speeding up work.

The CLAUDE.md file

CLAUDE.md is used to define rules for working with a specific repository. Such a file is not required for the first launch, but it helps make recurring tasks more predictable: the agent receives stable instructions about the stack, code conventions, and verification procedures.

In CLAUDE.md, you can record the stack in use, testing and build commands, the migration policy, a prohibition on editing production configuration, testing requirements, and rules for formatting changes. Do not put passwords, API keys, tokens, or information that should not enter an external context there.

MCP and external tools

The Model Context Protocol, or MCP, describes a way to connect external context sources and tools to an AI agent. Connecting an MCP server expands the model’s capabilities, but at the same time expands access to data and actions.

Check what operations each MCP server can perform, who owns its components, and what permissions it requests. An unverified server should not be connected to a corporate repository, cloud storage, or work accounts. Least-privilege access reduces the consequences of an error or compromised integration.

Claude Code in Russia: what to check before starting work

The availability of Anthropic services in Russia may depend on the account region, registration method, access model, and payment terms. Claude Code, the subscription, the web version, and the API may have different rules, so access through one method does not confirm the availability of another.

Before setting up a personal or corporate process, check Anthropic’s current terms, supported authentication options, and applicable usage rules. Do not build a process on the assumption that free mode, a specific model, or the selected plan will remain available permanently.

A corporate project requires separate approval under internal information security rules. The technical lead must understand which files the tool can see, which external integrations are connected, and who is responsible for reviewing the changes.

Code and data security when working with an AI agent

Claude Code works with project content, so the same least-privilege principles that apply to other external development services should be used with it. The convenience of repository analysis does not eliminate the need to control what information enters the model’s context.

What data should not be sent into the context

• Passwords, API keys, private keys, and access tokens.
• The contents of .env files and configuration containing real credentials.
• Database dumps, payment information, and logs containing customer identifiers.
• Personal data of employees, users, and counterparties.
• Confidential business documents, if their transfer has not been approved internally.

Even a test file can contain a real email address, phone number, or fragment of a customer export. Before launching, check the contents of the working directory and replace sensitive data with synthetic examples if the task allows such a substitution.

How to reduce risks

A separate Git branch makes it possible to see the full set of changes and quickly abandon an unsuccessful solution. Analysis and planning mode reduces the likelihood of unexpected editing. The diff, tests, and manual review help find errors that the model missed because of incomplete context or an incorrect hypothesis.

Limit the scope of the task to one module, one bug, or one type of change. Do not grant external integrations more permissions than the scenario requires. For the team, it is useful to define in advance which repositories may be analyzed by AI tools and who reviews the pull request after the agent’s work.

What companies should consider

If personal data enters prompts, files, logs, or test sets, take into account the requirements of Federal Law No. 152-FZ “On Personal Data.” Real customer exports, credentials, documents, and databases should not be sent to an external AI tool without assessing the legal basis and applicable protection measures.

Source code, documentation, and development results may also be subject to exclusive rights under Part Four of the Civil Code of the Russian Federation. Before using an external model in a corporate project, check internal policies, contractual restrictions, trade secret protections, and rules for processing information.

Common mistakes when using Claude Code

1. Running it in the working branch without Git control. Changes become mixed with ongoing development, and reverting them becomes more difficult.
2. An overly broad task without boundaries. The agent may change unrelated components or choose the wrong level of solution.
3. Accepting changes without reviewing the diff. Even a small patch can change behavior in a critical branch of the logic.
4. Skipping tests after making changes. The code may look convincing but violate existing scenarios.
5. Transferring secrets and real databases. The convenience of debugging does not justify exposing confidential data.

Git remains the primary mechanism for controlling changes. It does not hinder the work of an AI agent; instead, it makes that work verifiable: the developer sees every file and every line and can package the changes into a separate pull request.

FAQ

Can Claude Code be used for free?

Free modes, restrictions, available models, and terms of use depend on Anthropic’s current policy. Before starting work, check the current Claude Code terms separately from the subscription and API terms.

How does Claude Code differ from Claude in the browser?

Claude in the browser is suitable for conversations, text analysis, and individual code fragments that the user submits in the chat. Claude Code is designed to work with a project through the terminal, file structure, and tasks in the working directory.

How do you use Claude Code in VS Code?

Open the repository in VS Code, go to the integrated terminal, and launch Claude Code from the root folder. Check changes using the editor’s Git tools, then run the tests and review the result manually.

Does code need to be checked after Claude Code?

Yes. An AI agent speeds up the preparation of changes, but it does not replace testing, review, security checks, or the developer’s responsibility for the final code. The minimum set of controls includes the diff, running the tests, and manually evaluating the logic.

Is a CLAUDE.md file required for the first launch?

No, the first launch is possible without it. CLAUDE.md becomes useful later, when the team needs to record verification commands, architectural constraints, testing requirements, and rules for editing a specific repository.

Can a corporate repository be given to Claude Code?

The decision depends on the company’s internal policies, contractual obligations, data composition, and access settings. Before working with closed-source code, assess the risks to trade secrets, personal data, secrets, and rights to development results.

• Claude Code should be used as a managed assistant, not as an autonomous developer.
• A safe start begins with repository analysis and a limited task.
• Git, diffs, tests, and manual review remain mandatory parts of the control process.
• Anthropic’s availability in Russia and the access terms should be checked before setting up the workflow.

SEO Mind42 recommends starting with small tasks and gradually documenting the rules for working with the AI agent in the repository documentation. This approach gives the model useful context while allowing the team to retain control over the code, data, and final result.

Paid access to Anthropic models (Claude)

Official Anthropic prices and partner prices through Clodex are shown in the table below. For example, Claude Opus 5 through a partner is 5,9 times cheaper than the official price.