Claude Code Changed How I Write Software. Here's My Setup.

#ai #programming #productivity #tutorial

Software development demands efficiency and precision. Integrating AI into the development loop promises significant productivity gains. Many developers experiment with AI chatbots, but true transformation requires a structured approach. Claude Code offers this structure, moving beyond simple chat interfaces to an AI-native development environment.

For months, I have embedded Claude Code into my daily workflow. This isn't about using a large language model (LLM) as a glorified search engine or a quick code snippet generator. It's about leveraging an AI that understands project context, adheres to architectural patterns, and automates complex tasks. My setup has dramatically increased my output, reducing boilerplate and accelerating iteration cycles. Here is the exact configuration and workflow that made me 10x more productive.

What Claude Code Is

Claude Code is not merely a chatbot with a code interpreter. It is an integrated development environment (IDE) designed from the ground up to collaborate with Anthropic's Claude LLM. This environment provides specific features that allow the AI to operate within a defined project context, interact with files, and execute custom commands.

It differentiates itself through its deep understanding of project structure and its ability to maintain persistent context across sessions. This allows for complex, multi-step tasks that traditional chat interfaces struggle with. Claude Code functions as an intelligent co-pilot, not just a suggestion engine.

The Power of CLAUDE.md

The cornerstone of an effective Claude Code setup is the CLAUDE.md file. This isn't just a README; it's the project's constitution for the AI. It provides Claude with a comprehensive understanding of the project's architecture, goals, constraints, and preferred coding styles.

CLAUDE.md acts as a dynamic prompt, ensuring Claude always operates with the latest, most relevant project context. This eliminates the need to repeatedly provide background information.

I place CLAUDE.md at the root of every project. It contains sections for high-level goals, architectural decisions, technology stack, coding standards, and even specific modules or files the AI should prioritize. Updating this file updates Claude's understanding of the entire project automatically.

Here is a typical CLAUDE.md structure I use:

# Project: User Management Service

## [CONTEXT]
This service manages user authentication, authorization, and profile data. It integrates with an existing API Gateway and a PostgreSQL database. All communication is RESTful JSON. Security and performance are paramount.

## [GOALS]
- Implement robust user registration and login flows.
- Provide endpoints for user profile management (CRUD operations).
- Ensure all API endpoints are secured with JWT tokens.
- Maintain high test coverage (>90%).
- Deliver a scalable and maintainable codebase.

## [ARCHITECTURAL_GUIDELINES]
- Microservice architecture.
- Stateless service design.
- Event-driven patterns for asynchronous tasks (e.g., email verification).
- Use dependency injection for all services and repositories.

## [TECHNOLOGY_STACK]
- **Language:** Python 3.10+
- **Framework:** FastAPI
- **Database:** PostgreSQL (via SQLAlchemy 2.0 ORM)
- **Authentication:** PyJWT
- **Testing:** Pytest, httpx
- **Linting/Formatting:** Black, Pylint

## [CODING_STANDARDS]
- Adhere to PEP 8.
- Use type hints extensively.
- Docstrings for all functions, classes, and modules.
- Prefer explicit over implicit.
- Error handling must be explicit and informative.
- Avoid global state.

## [IMPORTANT_FILES_OR_MODULES]
- `app/main.py`: Main FastAPI application entry point.
- `app/schemas/`: Pydantic models for request/response validation.
- `app/crud/`: Database interaction logic.
- `app/services/`: Business logic.
- `app/api/v1/endpoints/`: API route definitions.
- `app/core/security.py`: JWT handling and password hashing.

## [CONSTRAINTS]
- Response times for critical endpoints must be under 50ms.
- Database queries must be optimized; avoid N+1 problems.
- All sensitive data must be encrypted at rest and in transit.
- No external dependencies without explicit approval.

## [PREVIOUS_DECISIONS]
- Chosen UUIDs for primary keys in all database tables.
- Implemented a custom rate-limiting middleware.
- Using `loguru` for structured logging.

This detailed CLAUDE.md provides Claude with a complete operational blueprint. When I ask Claude to "implement user registration," it immediately understands the technology stack, architectural patterns, and even specific file locations. This drastically reduces the back-and-forth common with less structured AI interactions.

Custom Slash Commands for Repetitive Tasks

Repetitive development tasks are prime candidates for automation. Claude Code allows defining custom slash commands, which map to predefined prompts or sequences of actions. These commands streamline common operations, ensuring consistency and saving significant time.

I configure these commands in a claude_config.json file, typically located in my user's Claude Code configuration directory. Each command specifies a name, a description, and the underlying prompt template or script to execute.

Here are some of the custom slash commands I use daily:

/test_suite: Runs all tests in the current directory, then analyzes failures and suggests fixes.
/refactor_file <filename>: Analyzes a specified file for potential refactorings (readability, performance, adherence to standards) and proposes changes.
/generate_docs <module_name>: Creates or updates Sphinx/MkDocs-style documentation for a given Python module.
/optimize_query <sql_query>: Analyzes a SQL query for performance bottlenecks and suggests indexing strategies or query rewrites.
/create_endpoint <resource_name>: Generates boilerplate for a new FastAPI CRUD endpoint, including Pydantic schemas, CRUD operations, and route definitions.

Consider the /create_endpoint command. Instead of manually creating files and writing boilerplate, I type /create_endpoint product. Claude Code then leverages the CLAUDE.md context (FastAPI, SQLAlchemy, Pydantic) to generate the necessary files.

An example entry in claude_config.json for a custom command might look like this:

{
  "commands": [
    {
      "name": "create_endpoint",
      "description": "Generates a new FastAPI CRUD endpoint boilerplate.",
      "template": "Based on the CLAUDE.md context, generate a complete FastAPI CRUD endpoint for the resource '{{resource_name}}'. Include Pydantic schemas (request and response), SQLAlchemy CRUD operations, and the API router definitions. Ensure type hints and docstrings are present. Provide the code for app/schemas/{{resource_name}}.py, app/crud/{{resource_name}}.py, and app/api/v1/endpoints/{{resource_name}}.py. Use UUIDs for IDs.",
      "args": [
        {
          "name": "resource_name",
          "type": "string",
          "description": "The name of the resource (e.g., 'user', 'product')."
        }
      ]
    },
    {
      "name": "refactor_file",
      "description": "Analyzes a file for refactoring opportunities.",
      "template": "Analyze the file '{{filename}}' for code smells, potential performance improvements, and adherence to the coding standards defined in CLAUDE.md. Propose specific, actionable refactorings, showing both the original and modified code snippets. Focus on readability, maintainability, and efficiency.",
      "args": [
        {
          "name": "filename",
          "type": "string",
          "description": "The path to the file to refactor."
        }
      ]
    }
  ]
}

This configuration allows me to invoke commands like /create_endpoint product or /refactor_file app/services/user_service.py. Claude Code parses the command, substitutes arguments into the template, and executes the refined prompt against the current project context. This automation ensures consistency and reduces manual effort significantly.

MCP Servers for External Tool Integration

Claude Code's real power extends beyond internal code generation through its Multi-Context Proxy (MCP) servers. MCP servers are lightweight HTTP services that act as bridges, allowing Claude Code to interact with external tools, APIs, databases, or even local system commands. This integrates the AI into a broader ecosystem, enabling it to perform actions beyond just generating text.

MCP servers empower Claude Code to "do" things in the real world, not just "suggest" them.

I use MCP servers to query internal knowledge bases, trigger CI/CD pipelines, interact with cloud provider APIs, or even perform database migrations. Each MCP server exposes a simple API that Claude Code can call with structured requests.

Here's an example of a simple Python-based MCP server that allows Claude Code to look up documentation for Python packages:

# mcp_doc_server.py
from flask import Flask, request, jsonify
import subprocess
import json

app = Flask(__name__)

@app.route('/package_docs', methods=['POST'])
def get_package_docs():
    data = request.json
    package_name = data.get('package_name')
    if not package_name:
        return jsonify({"error": "package_name is required"}), 400

    try:
        # Example: Use pip show to get basic package info
        # For full documentation, this would integrate with a more sophisticated system
        result = subprocess.run(['pip', 'show', package_name], capture_output=True, text=True, check=True)
        return jsonify({"package_name": package_name, "docs": result.stdout}), 200
    except subprocess.CalledProcessError as e:
        return jsonify({"error": f"Could not find documentation for {package_name}: {e.stderr}"}), 404
    except Exception as e:
        return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500

if __name__ == '__main__':
    # Run the MCP server on a specific port
    app.run(port=5001, debug=False)

To enable Claude Code to use this, I would configure it in claude_config.json to define the MCP server and an associated slash command:

{
  "mcp_servers": [
    {
      "name": "doc_lookup_service",
      "url": "http://localhost:5001",
      "description": "Provides documentation lookup for Python packages."
    }
  ],
  "commands": [
    {
      "name": "get_package_docs",
      "description": "Retrieves documentation for a specified Python package.",
      "template": {
        "mcp_server": "doc_lookup_service",
        "endpoint": "/package_docs",
        "method": "POST",
        "payload": {
          "package_name": "{{package_name}}"
        }
      },
      "args": [
        {
          "name": "package_name",
          "type": "string",
          "description": "The name of the Python package."
        }
      ]
    }
  ]
}

Now, I can type /get_package_docs fastapi directly within Claude Code. Claude Code sends a POST request to http://localhost:5001/package_docs with {"package_name": "fastapi"}. The MCP server processes this, retrieves the pip show output, and returns it to Claude Code. Claude then incorporates this information into its responses, for example, by summarizing the package details or suggesting usage examples based on the retrieved documentation.

This integration is powerful. It moves Claude Code from a purely generative tool to an actionable agent within my development environment.

Session Management for Long-Running Projects

Maintaining context over long development cycles is critical. Claude Code's session management ensures that the AI retains its understanding of the project, conversation history, and active tasks across multiple interactions and even days. Losing context means repeatedly re-explaining the project state, which wastes time and dilutes efficiency.

Persistent sessions prevent context drift, allowing Claude Code to pick up exactly where it left off, even after a break.

I manage sessions by creating a new session for each major feature or bug fix branch. When I start a new task, I load the relevant session or create a new one. This keeps the AI's focus narrow and relevant to the current work.

The process involves:

Saving a session: When a task is paused or completed, I save the current Claude Code session. This includes the entire conversation history, any temporary files Claude created, and its current internal state regarding the project.
Loading a session: When returning to a task, I load the corresponding session. Claude Code immediately restores its context, remembering previous discussions, code snippets, and decisions.

This capability is particularly useful for large projects with complex interdependencies. Claude remembers architectural nuances, design choices, and even past refactoring discussions, ensuring continuity throughout the development lifecycle. It prevents "AI amnesia" that plagues many other LLM interactions.

My Exact Daily Workflow

My daily workflow with Claude Code is highly structured, leveraging CLAUDE.md, custom commands, MCP servers, and session management. This integrated approach allows me to tackle complex tasks with unprecedented speed and consistency.

Here's a step-by-step breakdown of a typical development day:

Morning Setup and Session Load:
- I start Claude Code and load the session corresponding to my current Git branch or feature. If it's a new feature, I create a new session named after the branch (e.g., feature/user-profile-editing).
- Claude Code automatically loads the project's CLAUDE.md file, providing immediate context.
Task Definition and Initial Brainstorming:
- I articulate the task to Claude Code. For example: "Implement the update_user_profile endpoint. It should allow users to change their name and email. Ensure email uniqueness and proper validation."
- Claude Code, referencing CLAUDE.md, suggests the relevant files to modify (schemas, crud, services, endpoints) and potential security considerations. We iterate on the API design.
Code Generation and Iteration:
- I use custom commands to generate boilerplate. For instance, /create_endpoint user_profile might be too broad, so I'd ask Claude directly to generate specific Pydantic models for the update request.
- Claude generates the initial code for the Pydantic schemas, service logic, and endpoint definition.
- I review the generated code, making minor adjustments. I then ask Claude to "Refine app/services/user_service.py to handle unique email constraint errors gracefully."
Testing and Debugging:
- Once the code is generated, I initiate testing. I use the /test_suite command. Claude Code executes the tests, then analyzes the output.
- If tests fail, Claude Code highlights the failing tests and proposes specific fixes within the relevant files. For example: "The test test_update_user_profile_invalid_email failed. The current validation in app/services/user_service.py does not correctly handle existing email addresses. Here's a proposed fix:"
- I iterate with Claude, asking it to "Generate unit tests for the update_user_profile function in app/services/user_service.py."
Documentation and Refinement:
- After the feature is functional and tested, I use /generate_docs app/services/user_service.py to automatically update the documentation for the new functions.
- I then use /refactor_file app/api/v1/endpoints/user.py to ensure the new endpoint adheres to all coding standards and is as clean as possible.
External Interactions (MCP Servers):
- During development, if I need to look up a specific package's usage or query a staging database for existing user data patterns, I use MCP server commands like /get_package_docs pydantic or a custom /query_db <sql_statement> command to interact with a read-only staging database via an MCP server. This provides real-time data or context without leaving the Claude Code environment.
Saving Session and Committing:
- Once the feature is complete and all checks pass, I save the current Claude Code session.
- I then commit my code to Git, often asking Claude to "Generate a concise Git commit message summarizing the changes for the 'update user profile' feature."

This workflow ensures that Claude Code is deeply integrated into every stage of development. It acts as an intelligent assistant, from initial design to final documentation, constantly informed by the project's context and capable of executing complex tasks.

Takeaway and Next Steps

Claude Code, with its structured approach to AI-assisted development, transforms how engineers build software. The combination of a definitive CLAUDE.md for context, customizable slash commands for automation, MCP servers for real-world integration, and robust session management creates an environment where the AI is a true co-developer. It is not merely a tool for generating snippets, but a partner that understands, executes, and learns within your project's ecosystem.

To start leveraging Claude Code effectively, begin by:

Defining your CLAUDE.md: Invest time in clearly articulating your project's context, goals, and constraints. This is the most critical step for effective AI interaction.
Identifying repetitive tasks: Pinpoint common actions in your workflow that can be automated with custom slash commands. Start with simple ones like generating tests or boilerplate.
Exploring MCP server potential: Consider what external tools or data sources would benefit from direct AI interaction. Even a simple local script can unlock significant value.

Embrace Claude Code's structured environment. It's a fundamental shift in how you interact with AI, moving from ad-hoc prompting to a systematic, context-aware development partnership.