Anthropic Revealed Best practices for agentic coding With Claude Code

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Anthropic Revealed Best practices for agentic coding With Claude Code
Anthropic Revealed Best practices for agentic coding With Claude Code

Anthropic Revealed Best practices for agentic coding With Claude Code

Okay, let's dive into the world of agentic coding with Anthropic's Claude Code! As a creative writer who loves exploring new tools that blend technology and creativity, I find the idea of AI coding assistants fascinating. It's like having a super-smart pair programmer available anytime. Anthropic recently shared some valuable insights on how their teams use Claude Code effectively, and I thought it would be great to break down these practices in a way that’s easy to understand, even if you're just starting to explore AI in your coding workflow.

Think of Claude Code as a command-line tool – a bit like the text-based interface many developers use daily, but supercharged with AI. It’s designed to be flexible, allowing you to integrate it into your existing habits rather than forcing you into a rigid new system. This flexibility is wonderful, but it also means there's a bit of learning involved to figure out how you personally want to use it best.

Anthropic’s engineers have been using Claude Code across different projects, languages, and setups, and they've noticed some common patterns that lead to better results. These aren't strict rules, but more like helpful starting points. Let's explore these tips together, thinking about how they might fit into your own coding adventures.

Getting Started: Setting Up Your Claude Code Environment

Before you jump into complex tasks, spending a little time setting up your environment can make a big difference. Claude Code works by gathering context from your project to understand what you're working on. Optimizing this setup helps Claude get the right information efficiently.

The Magic of CLAUDE.md Files

Imagine you have a new teammate joining your project. You'd probably give them a rundown of essential commands, coding style preferences, how to run tests, and maybe some quirks specific to the project, right? That's exactly what CLAUDE.md files do for Claude Code.

This is a special file that Claude automatically reads when you start a conversation in a project directory. It’s the perfect place to put information you want Claude to always keep in mind for that specific project.

What can you put in a CLAUDE.md file?

  • Common Commands: List frequently used terminal commands (npm run build, pytest -k my_test, etc.).
  • Core Files: Mention key files or utility functions Claude should be aware of.
  • Style Guidelines: Specify coding style preferences (e.g., “Use f-strings in Python,” “Prefer arrow functions in JavaScript”).
  • Testing Instructions: Explain how to run tests (e.g., “Run single tests with npm test -- path/to/test.js“).
  • Repository Etiquette: Note conventions like branch naming or merge strategies.
  • Setup Details: Include specifics about the development environment (e.g., required Node version, compiler flags).
  • Project Quirks: Document any unusual behaviors or warnings developers often encounter.

There’s no strict format, but keeping it clear and concise, much like you would for a human colleague, works best. Here’s a simple example:

# Key Commands
- `make build`: Builds the main application.
- `make test`: Runs the full test suite.

# Coding Style
- Follow PEP 8 guidelines for Python.
- Keep functions reasonably short.

# Workflow Notes
- Always run `make lint` before committing changes.
- For UI changes, check behavior in both Chrome and Firefox.

Where do these files go?

  1. Project Root: The most common place is the main directory of your repository (where you usually run claude). Name it CLAUDE.md and check it into your version control (like Git) so your whole team can benefit. If you want project-specific notes that aren't shared, you can use CLAUDE.local.md and add it to your .gitignore file.
  2. Parent Directories: If you're working in a monorepo (a single repository containing multiple projects), you might have a main CLAUDE.md at the root and another one inside a specific sub-project directory (e.g., root/project-a/CLAUDE.md). Claude will read both when you run it from root/project-a.
  3. Child Directories: Conversely, Claude can pull in CLAUDE.md files from subdirectories on demand when you start working with files inside them.
  4. Home Folder: A global CLAUDE.md file located at ~/.claude/CLAUDE.md will apply to all your Claude sessions, regardless of the project.

Don't worry about creating these from scratch! When you run the /init command within Claude Code, it can automatically generate a basic CLAUDE.md file for you to start customizing.

Fine-Tuning Your CLAUDE.md

Think of your CLAUDE.md file not just as a static document, but as an evolving set of instructions. Just adding a ton of information isn't always the most effective approach. It's better to refine it over time.

  • Iterate: See how Claude responds to the instructions. If it's not following a guideline consistently, try rephrasing it or adding emphasis (Anthropic suggests using “IMPORTANT” or “YOU MUST” sometimes).
  • Use the # Key: While coding, you can press the # key within the Claude Code interface to give Claude an instruction (like a new command or style preference). It will automatically try to add this information to the relevant CLAUDE.md file. This makes it easy to update the file as you work.
  • Share Improvements: When you find phrasing that works well, commit the changes to your CLAUDE.md file so your teammates benefit too.

Managing Permissions: The Allowlist

Safety is a priority with tools like Claude Code. By default, it will ask for your permission before performing actions that could modify your system, like writing files or running certain commands. This is a good, cautious approach.

However, you can customize this behavior by creating an “allowlist” of tools or commands you trust. This can streamline your workflow for actions you know are safe or easily reversible.

How to manage the allowlist:

  1. During Session: When Claude asks for permission, you might see an option like “Always allow” for that specific action.
  2. /allowed-tools Command: After starting Claude Code, use this command to add or remove permissions. For example:
    • Edit: Always allow Claude to edit files.
    • Bash(git commit:*): Allow Claude to run git commit commands specifically.
    • mcp__puppeteer__puppeteer_navigate: Allow a specific tool from an MCP server (more on MCP later).
  3. Configuration File: You can manually edit your settings.json file (either project-specific in .claude/settings.json or global in ~/.claude.json). Putting it in the project's .claude directory and checking it into Git is a good way to share trusted tool settings with your team.
  4. Command-Line Flag: For session-specific permissions, you can use the --allowedTools flag when launching Claude Code.

GitHub Integration: Installing the gh CLI

If you work with GitHub, installing the official GitHub command-line interface (gh) is highly recommended. Claude Code knows how to use gh to perform actions like creating issues, opening pull requests, reading comments, and more, making interactions smoother. If gh isn't installed, Claude might still be able to use the GitHub API or an MCP server if you have those set up, but gh is often the most direct route.

Expanding Claude's Capabilities: Giving It More Tools

Claude Code operates within your computer's shell environment (like Bash or Zsh). This means it can use the tools you already have installed. You can also teach it to use custom scripts or connect it to more complex external tools.

Working with Bash Tools

Claude is familiar with many standard command-line utilities (like ls, grep, git, etc.) and the gh CLI we just mentioned. But what about your own custom scripts or less common tools? Claude won't know about them automatically.

How to teach Claude about your tools:

  • Tell it Directly: Mention the tool's name and provide examples of how to use it in your prompt.
  • Suggest --help: Ask Claude to run the tool with the --help flag to learn about its options.
  • Document in CLAUDE.md: For tools you use frequently with Claude, add their names and basic usage instructions to your CLAUDE.md file.

Leveraging MCP (Meta-Claude Protocol)

MCP sounds technical, but the core idea is simple: it's a way for different tools and services to communicate with Claude Code. Claude Code can act as both a server (allowing other tools to talk to it) and a client (allowing it to talk to other tools).

As a client, Claude can connect to MCP servers to gain new abilities. For example, you might have an MCP server that lets Claude control a web browser (like Puppeteer) or interact with an API (like Sentry for error tracking).

How to connect Claude to MCP servers:

  1. Project Configuration: Define connections specific to a project.
  2. Global Configuration: Set up connections available across all your projects.
  3. .mcp.json File: Create a .mcp.json file in your repository. If you check this file into Git, anyone working on the codebase can easily enable those MCP connections for their Claude Code sessions. This is great for team-wide tools.

If you're setting up MCP, using the --mcp-debug flag when launching Claude Code can help you troubleshoot connection issues.

Creating Custom Slash Commands

Do you find yourself asking Claude to perform the same multi-step task repeatedly? Maybe a specific debugging routine, log analysis process, or code refactoring pattern? You can save these workflows as custom slash commands.

  • How it works: Create Markdown files (.md) inside a .claude/commands folder in your project directory (for project-specific commands) or in ~/.claude/commands (for personal commands available everywhere).
  • File Content: Write the prompt template inside the Markdown file. You can use the special keyword $ARGUMENTS to pass parameters when you invoke the command.
  • Using the Command: When you type / in the Claude Code input, your custom commands will appear in the menu (e.g., /project:your-command-name or /user:your-command-name).

Example Custom Command:

Let's say you often ask Claude to fetch a GitHub issue, fix it, and create a pull request. You could create a file named .claude/commands/fix-github-issue.md with content like this:

Please analyze and fix the GitHub issue number: $ARGUMENTS.

Here's the process:
1. Use `gh issue view $ARGUMENTS` to understand the issue details.
2. Search the codebase to find the relevant files.
3. Implement the code changes needed to resolve the issue.
4. Write tests to confirm the fix works correctly.
5. Ensure the code passes all linting and type checks.
6. Write a clear commit message describing the fix.
7. Push the changes and open a pull request using `gh pr create`.

Remember to use the GitHub CLI (`gh`) for all GitHub interactions.

Now, in Claude Code, you could type /project:fix-github-issue 123 to have Claude attempt to fix issue #123 following these steps. This saves you from typing out the detailed instructions every time.

Finding Your Flow: Common Claude Code Workflows

One of the strengths of Claude Code is its lack of opinionated workflows. You can use it however suits you best. That said, the Anthropic team has observed several common patterns that tend to work well for different kinds of tasks.

The Explore, Plan, Code, Commit Cycle

This is a versatile approach suitable for many coding problems, especially those that aren't immediately obvious.

  1. Explore: Ask Claude to read relevant parts of the codebase. You can point it to specific files (read main.py) or give general directions (find the code related to user authentication). Crucially, tell it not to write any code yet. This phase is about understanding the current state. Using “subagents” (asking Claude to delegate parts of the investigation to specialized instances of itself) can be helpful here for complex exploration without using up too much main conversation context.
  2. Plan: Ask Claude to outline a plan to tackle the problem. Using trigger words like “think,” “think hard,” “think harder,” or even “ultrathink” gives Claude more time to analyze options. Review the plan. Does it make sense? Is it missing anything? You might even ask Claude to create a document or a GitHub issue with its plan so you have a reference point.
  3. Code: If the plan looks good, ask Claude to implement it. You can encourage it to verify its work as it goes (e.g., “implement the function and then write a small test for it”).
  4. Commit: Once the code is working and you're satisfied, ask Claude to commit the changes, perhaps write a commit message, and maybe even create a pull request or update documentation like READMEs.

The exploration and planning steps are key here. Without them, Claude might jump straight to coding, which can work for simple tasks but often leads to less optimal solutions for more complex problems.

Test-Driven Development (TDD) with Claude

If you like writing tests first (or want to try it), Claude Code can be a powerful partner. TDD works especially well with AI because tests provide a clear target for the AI to aim for.

  1. Write Tests: Ask Claude to write tests based on requirements or expected input/output. Be explicit that you're doing TDD and that it shouldn't write the actual implementation code yet, even if functions don't exist.
  2. Confirm Failure: Tell Claude to run the tests and verify that they fail (as expected, since the code isn't written). Again, remind it not to write implementation code yet.
  3. Commit Tests: Once you're happy with the tests, ask Claude to commit them.
  4. Write Code: Now, ask Claude to write the code necessary to make the tests pass. Instruct it not to modify the tests themselves. Tell it to iterate – write code, run tests, adjust code, run tests again – until all tests pass. Asking it to use subagents to double-check that the code isn't just “cheating” to pass the specific tests can be a good idea.
  5. Commit Code: When the tests pass and you're satisfied with the implementation, ask Claude to commit the code.

Having a clear success condition (passing tests) allows Claude to self-correct and improve its code incrementally.

Visual Iteration: Code, Screenshot, Refine

Similar to using tests as a target, you can use visual targets, especially for front-end or UI development.

  1. Provide Visual Input: Give Claude a way to “see” the desired outcome and its current progress.
    • Set up a tool (perhaps via MCP) that allows Claude to take browser screenshots.
    • Manually take screenshots and paste them into the Claude Code prompt.
    • Paste or drag-and-drop a design mock (image file) into the prompt, or provide the file path.
  2. Implement and Iterate: Ask Claude to implement the design in code. Then, instruct it to take a screenshot of its result (or you provide it) and compare it to the target mock. Ask it to keep refining the code until the output closely matches the desired visual.
  3. Commit: Once the visual output looks correct, ask Claude to commit the changes.

Like many creative processes, coding often benefits from iteration. Claude's first attempt might be okay, but giving it the ability to see its output and refine it usually leads to much better results.

“Safe YOLO” Mode: Letting Claude Run Freely (Carefully!)

Sometimes, you have repetitive, low-risk tasks where you trust Claude to handle them without constant supervision (like fixing hundreds of minor linting errors or generating boilerplate code). For these cases, you can use the --dangerously-skip-permissions flag when starting Claude Code. This tells Claude to bypass all permission checks and just get the job done.

⚠️ A Big Word of Caution: This mode is called “dangerously-skip-permissions” for a reason. Letting an AI run arbitrary commands without supervision carries risks. It could potentially delete files, corrupt your system, or even accidentally leak data if manipulated (e.g., through prompt injection).

To use this mode more safely: Anthropic strongly recommends running Claude Code inside a containerized environment (like Docker) that has no internet access and limited access to your main file system. This creates a sandbox where Claude can work without posing a significant risk to your system or data. You can find reference implementations for setting this up (like using Docker Dev Containers). Use this mode with extreme caution and only when you understand the risks and have taken precautions.

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Codebase Q&A: Your AI Onboarding Buddy

Starting on a new project or exploring an unfamiliar part of a large codebase can be time-consuming. Claude Code can act like a knowledgeable teammate you can ask questions. You don't need special prompts; just ask naturally.

Examples of questions:

  • “How does the logging system work in this project?”
  • “Can you show me how to add a new API endpoint?”
  • “What's the purpose of this async move { ... } block on line 134 of network_utils.rs?”
  • “What edge cases does the PaymentProcessor class handle?”
  • “Why does line 333 call old_function() instead of new_function()?”
  • “Show me the Java equivalent of this Python code snippet from data_parser.py.”

Claude will use its agentic capabilities to search the codebase, read relevant files, and formulate answers. Many Anthropic engineers find this significantly speeds up their onboarding process and reduces the need to interrupt colleagues with basic questions.

Mastering Git with Claude

Many developers find Git commands powerful but sometimes tricky to remember or use correctly. Claude Code is quite proficient with Git operations.

How Claude can help with Git:

  • History Exploration: Ask questions about the project's history, like “What features were added in version 2.1?”, “Who primarily worked on the authentication module?”, or “Why was this database schema designed this way?” Prompt Claude specifically to look through Git history (git log, git blame, etc.).
  • Commit Messages: Ask Claude to write commit messages. It can look at your staged changes (git diff --staged) and recent project history to compose informative messages.
  • Complex Operations: Get help with tasks like reverting specific file changes, resolving merge or rebase conflicts, or comparing branches.

This can make interacting with Git feel more intuitive and less error-prone.

Streamlining GitHub Interactions

If you use GitHub, Claude Code (especially with the gh CLI installed) can automate many common tasks.

GitHub tasks Claude can handle:

  • Creating Pull Requests: Simply ask Claude to “create a PR” (it understands the shorthand). It will use the code changes and context to generate a title and description.
  • Addressing Code Review Comments: Point Claude to your PR and ask it to fix specific comments left by reviewers. It can implement the changes and push them back to the PR branch.
  • Fixing Build/Lint Errors: If your PR fails automated checks, ask Claude to investigate the logs and fix the underlying issues.
  • Issue Triage: Ask Claude to go through open issues in a repository, categorize them, add labels, or summarize them.

This offloads routine GitHub chores, freeing you up for more complex work.

Working with Jupyter Notebooks

For researchers and data scientists using Jupyter notebooks, Claude Code can be a valuable assistant.

  • Reading and Writing: Claude can understand the structure of .ipynb files and help you write or modify code cells and markdown text.
  • Interpreting Outputs: Claude can often interpret the outputs of cells, including plots and images embedded in the notebook. This can be helpful for quick analysis or debugging.
  • Aesthetic Improvements: Before sharing your notebook, you can ask Claude to clean it up, improve formatting, or even suggest ways to make data visualizations more “aesthetically pleasing” for a human audience.

A common workflow is having Claude Code open in one terminal window and the Jupyter notebook open in VS Code or a browser, allowing for easy back-and-forth.

Pro Tips: Optimizing Your Claude Code Experience

Across all these workflows, a few general tips can help you get better results more consistently.

Specificity is Key

While Claude is good at inferring intent, it can't read your mind. The more specific you are in your instructions, the higher the chance Claude will do exactly what you want on the first try. Clear directions upfront save time on corrections later.

Consider these examples:

Less SpecificMore Specific
“add tests for utils.js”“write a new Jest test case for the calculateTotal function in utils.js. Cover the edge case where the input array is empty. Avoid using mocks for internal helper functions.”
“why is this API so confusing?”“Look through the Git history for ApiService.java and summarize the key decisions or changes that led to its current API design.”
“add a notification component”“Examine how existing UI components like UserProfileWidget.vue are built on the dashboard page. Pay attention to the separation of logic and presentation. Follow that pattern to implement a new NotificationBell component using Vue and Tailwind CSS (no external libraries).”

Leverage Visuals: Give Claude Images

Claude's ability to process images is a powerful asset. Don't hesitate to provide visual context.

  • Paste Screenshots: Use your OS's screenshot-to-clipboard feature (like Cmd+Ctrl+Shift+4 on macOS, then Ctrl+V in Claude Code – note the Ctrl+V, not Cmd+V)
  • Drag and Drop: Drag image files directly onto the Claude Code input area.
  • File Paths: Provide the path to image files on your system.

This is especially useful for UI development (comparing code output to design mocks) or when analyzing visual data like charts. Even if you aren't providing images, telling Claude that the “visual appeal” or “aesthetic quality” of the output is important can sometimes help it prioritize human-friendly results.

Point Claude to the Right Files

When you want Claude to look at or modify specific files or folders, use tab-completion in the Claude Code input. As you type a path, hitting Tab will suggest completions, making it quick and accurate to reference resources anywhere in your repository. This helps focus Claude's attention.

Use URLs for External Context

Need Claude to understand information from a documentation page, a blog post, or an API spec? Just paste the URL directly into your prompt. Claude can fetch and read the content. If you find yourself repeatedly giving URLs from the same domain (e.g., docs.yourcompany.com), consider adding that domain to your allowed tools list using /allowed-tools to avoid permission prompts every time.

Course Correct Like a Co-Pilot

Think of yourself as an active collaborator with Claude, not just someone giving orders and waiting. Guiding Claude often leads to better results faster than letting it run autonomously for long periods (unless you're in that carefully controlled “Safe YOLO” mode).

Tools for Course Correction:

  1. Plan Review: Ask Claude to make a plan before coding, and only proceed once you approve it.
  2. Interrupt (Escape Key): If you see Claude heading in the wrong direction or want to add information mid-task, press the Escape key. This pauses Claude without losing the conversation context, allowing you to provide new instructions.
  3. Edit History (Double Escape): Press Escape twice to go back to your previous prompt. You can edit it and resubmit, effectively exploring a different path from that point in the conversation.
  4. Undo: Ask Claude to undo its recent changes if things went awry, often used in conjunction with interrupting or editing history to try a different approach.

While Claude can sometimes nail a task perfectly on the first try, using these correction techniques interactively usually yields superior solutions more efficiently.

Keep Context Clean with /clear

In long coding sessions, the conversation history, file contents Claude has read, and commands it has run can fill up its “context window” (the amount of information it can actively consider). Irrelevant information can sometimes slow down performance or even distract Claude. Use the /clear command periodically, especially when switching between distinct tasks, to reset the context and start fresh.

Checklists for Complex Tasks

For large, multi-step tasks like migrating code between frameworks, fixing hundreds of lint errors, or following a complex build process, using a checklist can significantly improve Claude's performance and reliability.

  • How: Ask Claude to create a Markdown file (or even a GitHub issue) to act as a checklist or scratchpad.
  • Example (Linting):
    1. Tell Claude to run the linter and write all reported errors (including file names and line numbers) into a Markdown checklist file.
    2. Instruct Claude to work through the checklist item by item: fix the issue, verify the fix, check off the item, and move to the next one.

This structured approach helps Claude stay organized and track progress on complex, exhaustive tasks.

Getting Data Into Claude

There are several ways to provide data for Claude to work with:

  1. Copy and Paste: The most common method for text, code snippets, or error messages.
  2. Piping: Use your shell's pipe (|) operator to send the output of a command directly into Claude Code (e.g., cat server.log | claude "Summarize the errors in this log"). Great for logs, CSVs, or large text files.
  3. Commands/Tools: Tell Claude to fetch data using bash commands (curl, cat), MCP tools, or custom slash commands you've created.
  4. Files/URLs: Ask Claude to read specific files or fetch content from URLs (works for text and images).

Often, a session might involve combining these methods – perhaps piping in a log file, then asking Claude to use git blame to find related code changes.

Taking it Further: Automation and Multi-Claude Setups

Once you're comfortable with the basics, you can explore more advanced techniques involving automation and even running multiple Claude instances together.

Headless Mode: Claude for Your Automation Needs

Claude Code isn't just for interactive use. It has a “headless” mode designed for scripts and automated processes like Continuous Integration (CI) pipelines, Git pre-commit hooks, or custom build tools.

  • How: Use the -p flag followed by your prompt (e.g., claude -p "Refactor this file to use async/await"). This runs Claude non-interactively.
  • Output: For programmatic use, the --output-format stream-json flag provides structured JSON output that's easier for scripts to parse.
  • Note: Headless mode runs once per invocation; it doesn't maintain a persistent session.

Automated Issue Triage

Headless mode is great for triggering actions based on events. For instance, the public Claude Code repository itself uses a GitHub Action that runs Claude in headless mode whenever a new issue is filed. Claude inspects the issue content and automatically assigns relevant labels (like “bug” or “feature request”).

Claude as an Advanced Linter

While traditional linters catch syntax errors and style violations, Claude can perform more subjective code reviews in an automated way. You could set up a pre-commit hook that runs Claude in headless mode to scan staged files for things like:

  • Typos in comments or variable names.
  • Stale or misleading comments.
  • Potentially confusing logic.
  • Inconsistent naming conventions.

Upleveling with Multi-Claude Workflows

Some of the most powerful applications involve orchestrating multiple Claude instances working in parallel or sequence.

The Verify Pattern: One Claude Writes, Another Checks

Just like having one engineer write code and another review it can catch issues, you can apply this pattern with Claude. Having separate instances often works better because they maintain distinct contexts.

  1. Write: Use one Claude instance to write the initial code or tests.
  2. Clear/Switch: Use /clear or open a new terminal with a separate Claude instance.
  3. Review/Test: Have the second Claude review the code written by the first or write tests for it.
  4. Integrate: Start another Claude instance (or /clear again). Give it both the original code and the review feedback (or the tests) and ask it to integrate the feedback or make the tests pass.

This separation can lead to more robust results than asking a single Claude instance to do everything.

Managing Multiple Checkouts

A straightforward way to work on multiple things in parallel is to simply create several copies (checkouts) of your repository in different folders.

  1. Clone your repo into project-a, project-b, project-c.
  2. Open each folder in a separate terminal tab.
  3. Start a Claude Code session in each tab, giving each one a different task.
  4. Cycle through the tabs to monitor progress and grant permissions.

Git Worktrees: A Lighter Alternative

Instead of full separate checkouts, Git's worktree feature offers a more efficient way to work on multiple branches simultaneously within the same repository clone. Each worktree has its own directory and set of files, but they all share the same underlying Git repository history.

This is ideal for running multiple independent Claude tasks in parallel without them interfering with each other.

  1. Create Worktrees: From your main repo directory, run commands like git worktree add ../project-feature-x feature-x-branch. This creates a new directory ../project-feature-x linked to the branch feature-x-branch.
  2. Launch Claude: cd ../project-feature-x and start claude.
  3. Repeat: Create more worktrees and launch Claude instances in them via separate terminal tabs.

Tips for using worktrees with Claude:

  • Use clear naming conventions for your worktree directories.
  • Keep one terminal tab dedicated to each worktree.
  • Consider using terminal notifications (like iTerm2 on Mac offers) to know when Claude needs your input.
  • Open each worktree in a separate IDE window if needed.
  • Clean up worktrees when you're done: git worktree remove ../project-feature-x.

Headless Mode with Custom Harnesses

You can build sophisticated automation by calling Claude Code programmatically (claude -p ...) from your own scripts. This lets you leverage Claude's capabilities within larger, custom workflows. Two main patterns emerge:

  1. Fanning Out: For tasks that involve processing many items independently (e.g., migrating thousands of files, analyzing hundreds of log entries).
    • Have Claude (or a script) generate a list of tasks (e.g., a list of file paths to migrate).
    • Write a script that loops through the list, calling claude -p "Your task prompt for $item" for each item. You can specify allowed tools (--allowedTools) for the headless run.
    • Run the script, refining the prompt until Claude consistently produces the desired outcome.
  2. Pipelining: Integrate Claude into an existing data processing pipeline.
    • Use shell pipes: your_data_source | claude -p "Process this data..." --output-format json | your_next_command. The JSON output makes it easy for subsequent steps to consume Claude's results.

Using the --verbose flag when calling headless Claude can be helpful for debugging your script's interaction with Claude Code. Remember to turn verbose mode off for cleaner output in production automation.

Wrapping Up

Anthropic's Claude Code presents an exciting way to integrate AI into the nuts and bolts of software development. While it's a powerful tool out of the box, adopting these best practices – customizing your setup, giving Claude the right tools and context, choosing appropriate workflows, optimizing your interactions, and even exploring advanced automation – can significantly elevate your experience.

The key seems to be thinking of Claude Code not just as a code generator, but as a flexible, agentic collaborator. Experiment with these suggestions, see what clicks with your personal style and the projects you work on, and discover your own best practices along the way.

If you want to read Anthropic's original post and explore their documentation further, you can find it here: https://www.anthropic.com/engineering/claude-code-best-practices

Happy coding!

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