Claude Code AI Assistant Cheat Sheet

Overview

Claude Code is an AI-powered code assistant developed by Anthropic that provides intelligent code completion, generation, debugging, and explanation capabilities. Built on Claude's advanced language model, it offers context-aware programming assistance across multiple languages and frameworks. Claude Code excels at understanding complex codebases, providing detailed explanations, and generating high-quality code that follows best practices and security guidelines.

⚠️ Usage Notice: Claude Code is designed to assist developers but should not replace proper code review, testing, and security practices. Always validate AI-generated code before production use.

Getting Started

Web Interface Access

bash

# Access Claude Code via web interface
# Navigate to: https://claude.ai
# Sign up for an account or log in
# Select "Claude 3" model for code assistance

# Features available in web interface:
# - Code generation and completion
# - Code explanation and documentation
# - Debugging assistance
# - Architecture recommendations
# - Code review and optimization

API Integration

python

# Install Anthropic Python SDK
pip install anthropic

# Basic API setup
import anthropic

client = anthropic.Anthropic(
    api_key="your-api-key-here"
)

# Example code generation request
response = client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=1000,
    messages=[
        {
            "role": "user", 
            "content": "Write a Python function to implement binary search"
        }
    ]
)

print(response.content[0].text)

IDE Integration Setup

bash

# VS Code Extension (unofficial community extensions)
# Search for "Claude" or "Anthropic" in VS Code marketplace

# Vim/Neovim integration
# Install via plugin manager (example with vim-plug)
Plug 'anthropic/claude.vim'

# Emacs integration
# Add to your .emacs or init.el
(require 'claude-mode)

# JetBrains IDEs
# Install Claude plugin from marketplace
# Configure API key in settings

Code Generation and Completion

Basic Code Generation

python

# Example prompts for code generation

# Function creation
prompt = """
Create a Python function that:
- Takes a list of dictionaries
- Filters by a specific key-value pair
- Returns sorted results by another key
- Includes error handling and type hints
"""

# Class implementation
prompt = """
Design a Python class for a REST API client that:
- Handles authentication
- Implements retry logic
- Supports async operations
- Includes comprehensive logging
"""

# Algorithm implementation
prompt = """
Implement a graph traversal algorithm that:
- Uses breadth-first search
- Finds shortest path between nodes
- Handles weighted edges
- Returns path and total cost
"""

Advanced Code Generation

python

#!/usr/bin/env python3
# claude-code-generator.py

import anthropic
import json
import os
from typing import Dict, List, Optional

class ClaudeCodeGenerator:
    def __init__(self, api_key: str):
        self.client = anthropic.Anthropic(api_key=api_key)
        self.conversation_history = []
    
    def generate_code(self, prompt: str, language: str = "python", 
                     context: Optional[str] = None) -> str:
        """Generate code based on prompt and context"""
        
        system_prompt = f"""
You are an expert {language} developer. Generate clean, efficient, 
and well-documented code that follows best practices. Include:
- Proper error handling
- Type hints (where applicable)
- Comprehensive docstrings
- Security considerations
- Performance optimizations
"""
        
        if context:
            full_prompt = f"Context:\n{context}\n\nRequest:\n{prompt}"
        else:
            full_prompt = prompt
        
        try:
            response = self.client.messages.create(
                model="claude-3-sonnet-20240229",
                max_tokens=2000,
                system=system_prompt,
                messages=[
                    {"role": "user", "content": full_prompt}
                ]
            )
            
            generated_code = response.content[0].text
            
            # Store in conversation history
            self.conversation_history.append({
                "prompt": prompt,
                "language": language,
                "response": generated_code,
                "timestamp": datetime.now().isoformat()
            })
            
            return generated_code
        
        except Exception as e:
            return f"Error generating code: {e}"
    
    def explain_code(self, code: str, language: str = "python") -> str:
        """Get detailed explanation of existing code"""
        
        prompt = f"""
Analyze this {language} code and provide:
1. High-level overview of functionality
2. Line-by-line explanation of complex parts
3. Potential improvements or issues
4. Security considerations
5. Performance analysis

Code:
```{language}
{code}

"""

    try:
        response = self.client.messages.create(
            model="claude-3-sonnet-20240229",
            max_tokens=1500,
            messages=[
                {"role": "user", "content": prompt}
            ]
        )
        
        return response.content[0].text
    
    except Exception as e:
        return f"Error explaining code: {e}"

def debug_code(self, code: str, error_message: str, 
               language: str = "python") -> str:
    """Debug code and suggest fixes"""
    
    prompt = f"""

Debug this {language} code that's producing an error:

Error message:

Code:

{code}

Please provide:

Root cause analysis
Specific fix recommendations
Corrected code

Prevention strategies for similar issues """

 try:
     response = self.client.messages.create(
         model="claude-3-sonnet-20240229",
         max_tokens=2000,
         messages=[
             {"role": "user", "content": prompt}
         ]
     )
     
     return response.content[0].text
 
 except Exception as e:
     return f"Error debugging code: {e}"

def optimize_code(self, code: str, language: str = "python") -> str: """Optimize code for performance and readability"""

 prompt = f"""

Optimize this {language} code for:

Performance improvements
Memory efficiency
Code readability
Maintainability
Security best practices

Original code:

{code}

Provide optimized version with explanations of changes. """

    try:
        response = self.client.messages.create(
            model="claude-3-sonnet-20240229",
            max_tokens=2000,
            messages=[
                {"role": "user", "content": prompt}
            ]
        )
        
        return response.content[0].text
    
    except Exception as e:
        return f"Error optimizing code: {e}"

def generate_tests(self, code: str, language: str = "python") -> str:
    """Generate comprehensive test cases"""
    
    framework_map = {
        "python": "pytest",
        "javascript": "jest",
        "java": "junit",
        "csharp": "nunit",
        "go": "testing"
    }
    
    framework = framework_map.get(language, "appropriate testing framework")
    
    prompt = f"""

Generate comprehensive test cases for this {language} code using {framework}:

Code to test:

{code}

Include:

Unit tests for all functions/methods
Edge cases and boundary conditions
Error handling tests
Integration tests (if applicable)
Performance tests (if relevant)
Mock objects where needed """
```
 try:
     response = self.client.messages.create(
         model="claude-3-sonnet-20240229",
         max_tokens=2000,
         messages=[
             {"role": "user", "content": prompt}
         ]
     )
     
     return response.content[0].text
 
 except Exception as e:
     return f"Error generating tests: {e}"
```
def save_conversation(self, filename: str = "claude_conversation.json"): """Save conversation history to file""" try: with open(filename, 'w') as f: json.dump(self.conversation_history, f, indent=2) print(f"Conversation saved to {filename}") except Exception as e: print(f"Error saving conversation: {e}")

Example usage

def main(): # Initialize with your API key api_key = os.getenv("ANTHROPIC_API_KEY") if not api_key: print("Please set ANTHROPIC_API_KEY environment variable") return

generator = ClaudeCodeGenerator(api_key)

# Generate a REST API client
prompt = """

Create a Python class for a REST API client that handles:

GET, POST, PUT, DELETE requests
Authentication with API keys
Retry logic with exponential backoff
Rate limiting
Response caching
Comprehensive error handling """
code = generator.generate_code(prompt, "python") print("Generated Code:") print("=" * 50) print(code)
Generate tests for the code
tests = generator.generate_tests(code, "python") print("\nGenerated Tests:") print("=" * 50) print(tests)
Save conversation
generator.save_conversation()

if name == "main": main()


## Multi-Language Support

### Python Development
```python
# Python-specific prompts and examples

# Data science and ML
prompt = """
Create a Python script for data analysis that:
- Loads data from CSV/JSON files
- Performs exploratory data analysis
- Creates visualizations with matplotlib/seaborn
- Implements basic ML models with scikit-learn
- Exports results and models
"""

# Web development with Flask/Django
prompt = """
Build a Flask web application that:
- Implements user authentication
- Uses SQLAlchemy for database operations
- Includes API endpoints with proper validation
- Implements middleware for logging and security
- Includes comprehensive error handling
"""

# DevOps and automation
prompt = """
Create a Python automation script that:
- Manages AWS resources using boto3
- Implements infrastructure as code
- Includes monitoring and alerting
- Handles secrets management
- Provides detailed logging and reporting
"""

JavaScript/TypeScript Development

javascript

// JavaScript/TypeScript prompts

// React application development
const prompt = `
Create a React TypeScript component that:
- Implements a data table with sorting and filtering
- Uses React hooks for state management
- Includes proper TypeScript interfaces
- Implements accessibility features
- Includes comprehensive error boundaries
`;

// Node.js backend development
const prompt = `
Build a Node.js Express API that:
- Implements JWT authentication
- Uses TypeScript for type safety
- Includes input validation with Joi
- Implements rate limiting and security middleware
- Uses Prisma for database operations
`;

// Frontend optimization
const prompt = `
Optimize this JavaScript code for:
- Performance improvements
- Bundle size reduction
- Memory leak prevention
- Browser compatibility
- Accessibility compliance
`;

Go Development

// Go-specific development prompts

// Microservices development
prompt := `
Create a Go microservice that:
- Implements gRPC and REST APIs
- Uses proper error handling patterns
- Includes comprehensive logging
- Implements health checks and metrics
- Uses dependency injection
`

// Concurrent programming
prompt := `
Implement a Go program that:
- Uses goroutines and channels effectively
- Implements worker pool pattern
- Handles graceful shutdown
- Includes proper synchronization
- Manages resource cleanup
`

Code Review and Analysis

Automated Code Review

python

#!/usr/bin/env python3
# claude-code-reviewer.py

import anthropic
import os
import subprocess
import json
from pathlib import Path

class ClaudeCodeReviewer:
    def __init__(self, api_key: str):
        self.client = anthropic.Anthropic(api_key=api_key)
    
    def review_file(self, file_path: str) -> Dict:
        """Review a single code file"""
        
        try:
            with open(file_path, 'r', encoding='utf-8') as f:
                code_content = f.read()
        except Exception as e:
            return {"error": f"Could not read file: {e}"}
        
        file_extension = Path(file_path).suffix
        language = self.detect_language(file_extension)
        
        prompt = f"""
Perform a comprehensive code review of this {language} file:

File: {file_path}

```{language}
{code_content}

Please analyze:

Code quality and best practices
Security vulnerabilities
Performance issues
Maintainability concerns
Documentation quality
Error handling
Testing coverage gaps
Specific improvement recommendations

Provide a structured review with severity levels (Critical, High, Medium, Low). """

    try:
        response = self.client.messages.create(
            model="claude-3-sonnet-20240229",
            max_tokens=2000,
            messages=[
                {"role": "user", "content": prompt}
            ]
        )
        
        return {
            "file": file_path,
            "language": language,
            "review": response.content[0].text,
            "status": "success"
        }
    
    except Exception as e:
        return {
            "file": file_path,
            "error": f"Review failed: {e}",
            "status": "error"
        }

def review_directory(self, directory: str, extensions: List[str] = None) -> List[Dict]:
    """Review all code files in a directory"""
    
    if extensions is None:
        extensions = ['.py', '.js', '.ts', '.go', '.java', '.cpp', '.c', '.cs']
    
    code_files = []
    for ext in extensions:
        code_files.extend(Path(directory).rglob(f"*{ext}"))
    
    reviews = []
    for file_path in code_files:
        print(f"Reviewing: {file_path}")
        review = self.review_file(str(file_path))
        reviews.append(review)
    
    return reviews

def review_git_diff(self, commit_range: str = "HEAD~1..HEAD") -> Dict:
    """Review changes in git diff"""
    
    try:
        # Get git diff
        result = subprocess.run(
            ["git", "diff", commit_range],
            capture_output=True,
            text=True,
            check=True
        )
        
        diff_content = result.stdout
        
        if not diff_content.strip():
            return {"message": "No changes found in the specified range"}
        
        prompt = f"""

Review this git diff for:

Code quality issues
Security vulnerabilities
Breaking changes
Performance implications
Documentation updates needed
Testing requirements

Git diff:

diff

{diff_content}

Focus on the changed lines and their impact on the codebase. """

        response = self.client.messages.create(
            model="claude-3-sonnet-20240229",
            max_tokens=2000,
            messages=[
                {"role": "user", "content": prompt}
            ]
        )
        
        return {
            "commit_range": commit_range,
            "review": response.content[0].text,
            "status": "success"
        }
    
    except subprocess.CalledProcessError as e:
        return {
            "error": f"Git command failed: {e}",
            "status": "error"
        }
    except Exception as e:
        return {
            "error": f"Review failed: {e}",
            "status": "error"
        }

def detect_language(self, file_extension: str) -> str:
    """Detect programming language from file extension"""
    
    language_map = {
        '.py': 'python',
        '.js': 'javascript',
        '.ts': 'typescript',
        '.go': 'go',
        '.java': 'java',
        '.cpp': 'cpp',
        '.c': 'c',
        '.cs': 'csharp',
        '.rb': 'ruby',
        '.php': 'php',
        '.rs': 'rust',
        '.kt': 'kotlin',
        '.swift': 'swift'
    }
    
    return language_map.get(file_extension.lower(), 'unknown')

def generate_review_report(self, reviews: List[Dict], output_file: str = "code_review_report.md"):
    """Generate a comprehensive review report"""
    
    report_lines = [
        "# Code Review Report",
        f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
        "",
        "## Summary",
        ""
    ]
    
    # Count issues by severity
    total_files = len([r for r in reviews if r.get('status') == 'success'])
    error_files = len([r for r in reviews if r.get('status') == 'error'])
    
    report_lines.extend([
        f"- Total files reviewed: {total_files}",
        f"- Files with errors: {error_files}",
        "",
        "## Detailed Reviews",
        ""
    ])
    
    for review in reviews:
        if review.get('status') == 'success':
            report_lines.extend([
                f"### {review['file']}",
                f"**Language**: {review['language']}",
                "",
                review['review'],
                "",
                "---",
                ""
            ])
        elif review.get('status') == 'error':
            report_lines.extend([
                f"### {review['file']} (ERROR)",
                f"**Error**: {review['error']}",
                "",
                "---",
                ""
            ])
    
    # Write report
    try:
        with open(output_file, 'w', encoding='utf-8') as f:
            f.write('\n'.join(report_lines))
        print(f"Review report saved to: {output_file}")
    except Exception as e:
        print(f"Error saving report: {e}")

def main(): import sys

api_key = os.getenv("ANTHROPIC_API_KEY")
if not api_key:
    print("Please set ANTHROPIC_API_KEY environment variable")
    return

reviewer = ClaudeCodeReviewer(api_key)

if len(sys.argv) < 2:
    print("Usage:")
    print("  python3 claude-code-reviewer.py <file_or_directory>")
    print("  python3 claude-code-reviewer.py --git-diff [commit_range]")
    return

if sys.argv[1] == "--git-diff":
    commit_range = sys.argv[2] if len(sys.argv) > 2 else "HEAD~1..HEAD"
    review = reviewer.review_git_diff(commit_range)
    
    if review.get('status') == 'success':
        print("Git Diff Review:")
        print("=" * 50)
        print(review['review'])
    else:
        print(f"Error: {review.get('error', 'Unknown error')}")

else:
    target = sys.argv[1]
    
    if os.path.isfile(target):
        # Review single file
        review = reviewer.review_file(target)
        
        if review.get('status') == 'success':
            print(f"Review for {target}:")
            print("=" * 50)
            print(review['review'])
        else:
            print(f"Error: {review.get('error', 'Unknown error')}")
    
    elif os.path.isdir(target):
        # Review directory
        reviews = reviewer.review_directory(target)
        reviewer.generate_review_report(reviews)
        
        print(f"Reviewed {len(reviews)} files")
        print("Detailed report saved to: code_review_report.md")
    
    else:
        print(f"Error: {target} is not a valid file or directory")

if name == "main": main()


## Architecture and Design Assistance

### System Architecture Design
```python
# Architecture design prompts

# Microservices architecture
prompt = """
Design a microservices architecture for an e-commerce platform that includes:
- User management service
- Product catalog service
- Order processing service
- Payment service
- Notification service
- API gateway configuration
- Database design for each service
- Inter-service communication patterns
- Security and authentication strategy
- Monitoring and logging approach
"""

# Database design
prompt = """
Design a database schema for a social media platform that supports:
- User profiles and relationships
- Posts, comments, and reactions
- Real-time messaging
- Content moderation
- Analytics and reporting
- Scalability considerations
- Data privacy compliance
"""

# API design
prompt = """
Design a RESTful API for a project management tool that includes:
- Resource endpoints and HTTP methods
- Request/response schemas
- Authentication and authorization
- Rate limiting strategy
- Error handling patterns
- API versioning approach
- Documentation structure
"""

Design Pattern Implementation

python

#!/usr/bin/env python3
# claude-design-patterns.py

import anthropic
import os

class ClaudeDesignPatternAssistant:
    def __init__(self, api_key: str):
        self.client = anthropic.Anthropic(api_key=api_key)
    
    def suggest_pattern(self, problem_description: str, language: str = "python") -> str:
        """Suggest appropriate design patterns for a problem"""
        
        prompt = f"""
Analyze this software design problem and suggest appropriate design patterns:

Problem: {problem_description}
Target Language: {language}

Please provide:
1. Recommended design patterns with explanations
2. Implementation examples in {language}
3. Pros and cons of each pattern
4. Alternative approaches
5. Best practices for implementation
"""
        
        try:
            response = self.client.messages.create(
                model="claude-3-sonnet-20240229",
                max_tokens=2000,
                messages=[
                    {"role": "user", "content": prompt}
                ]
            )
            
            return response.content[0].text
        
        except Exception as e:
            return f"Error getting pattern suggestions: {e}"
    
    def implement_pattern(self, pattern_name: str, context: str, language: str = "python") -> str:
        """Generate implementation of a specific design pattern"""
        
        prompt = f"""
Implement the {pattern_name} design pattern in {language} for this context:

Context: {context}

Please provide:
1. Complete implementation with proper class structure
2. Usage examples
3. Unit tests
4. Documentation and comments
5. Common pitfalls to avoid
"""
        
        try:
            response = self.client.messages.create(
                model="claude-3-sonnet-20240229",
                max_tokens=2000,
                messages=[
                    {"role": "user", "content": prompt}
                ]
            )
            
            return response.content[0].text
        
        except Exception as e:
            return f"Error implementing pattern: {e}"
    
    def refactor_with_patterns(self, code: str, language: str = "python") -> str:
        """Suggest refactoring existing code using design patterns"""
        
        prompt = f"""
Analyze this {language} code and suggest refactoring using appropriate design patterns:

Current code:
```{language}
{code}

Please provide:

Identified code smells and issues
Recommended design patterns for improvement
Refactored code implementation
Explanation of improvements

Migration strategy from old to new code """

 try:
     response = self.client.messages.create(
         model="claude-3-sonnet-20240229",
         max_tokens=2000,
         messages=[
             {"role": "user", "content": prompt}
         ]
     )
     
     return response.content[0].text
 
 except Exception as e:
     return f"Error refactoring code: {e}"

Example usage

def main(): api_key = os.getenv("ANTHROPIC_API_KEY") if not api_key: print("Please set ANTHROPIC_API_KEY environment variable") return

assistant = ClaudeDesignPatternAssistant(api_key)

# Example: Suggest patterns for a logging system
problem = """

I need to design a logging system that:

Supports multiple output destinations (file, console, database)
Allows different log levels (DEBUG, INFO, WARN, ERROR)
Can be configured at runtime
Supports custom formatting
Is thread-safe and performant """
suggestions = assistant.suggest_pattern(problem, "python") print("Design Pattern Suggestions:") print("=" * 50) print(suggestions)

if name == "main": main()


## Integration and Automation

### CI/CD Integration
```yaml
# GitHub Actions workflow for Claude Code integration
name: Claude Code Review

on:
  pull_request:
    branches: [ main, develop ]

jobs:
  claude-review:
    runs-on: ubuntu-latest
    
    steps:
    - uses: actions/checkout@v3
      with:
        fetch-depth: 0
    
    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: '3.9'
    
    - name: Install dependencies
      run: |
        pip install anthropic
    
    - name: Run Claude Code Review
      env:
        ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
      run: |
        python3 .github/scripts/claude-review.py
    
    - name: Comment PR
      uses: actions/github-script@v6
      with:
        script: |
          const fs = require('fs');
          const review = fs.readFileSync('claude-review-output.md', 'utf8');
          
          github.rest.issues.createComment({
            issue_number: context.issue.number,
            owner: context.repo.owner,
            repo: context.repo.repo,
            body: `## Claude Code Review\n\n${review}`
          });

VS Code Extension Integration

json

// VS Code settings for Claude integration
{
  "claude.apiKey": "${env:ANTHROPIC_API_KEY}",
  "claude.model": "claude-3-sonnet-20240229",
  "claude.maxTokens": 1000,
  "claude.autoComplete": true,
  "claude.codeReview": true,
  "claude.explainCode": true,
  "claude.generateTests": true,
  "claude.languages": [
    "python",
    "javascript",
    "typescript",
    "go",
    "java",
    "cpp"
  ]
}

Command Line Tool

bash

#!/bin/bash
# claude-cli.sh - Command line interface for Claude Code

CLAUDE_API_KEY="${ANTHROPIC_API_KEY}"
CLAUDE_MODEL="claude-3-sonnet-20240229"

if [ -z "$CLAUDE_API_KEY" ]; then
    echo "Error: ANTHROPIC_API_KEY environment variable not set"
    exit 1
fi

case "$1" in
    "generate")
        if [ -z "$2" ]; then
            echo "Usage: claude-cli.sh generate <prompt>"
            exit 1
        fi
        python3 -c "
import anthropic
client = anthropic.Anthropic(api_key='$CLAUDE_API_KEY')
response = client.messages.create(
    model='$CLAUDE_MODEL',
    max_tokens=1000,
    messages=[{'role': 'user', 'content': '$2'}]
)
print(response.content[0].text)
"
        ;;
    
    "explain")
        if [ -z "$2" ]; then
            echo "Usage: claude-cli.sh explain <file>"
            exit 1
        fi
        
        if [ ! -f "$2" ]; then
            echo "Error: File $2 not found"
            exit 1
        fi
        
        code_content=$(cat "$2")
        python3 -c "
import anthropic
client = anthropic.Anthropic(api_key='$CLAUDE_API_KEY')
response = client.messages.create(
    model='$CLAUDE_MODEL',
    max_tokens=1500,
    messages=[{
        'role': 'user', 
        'content': 'Explain this code:\n\n$code_content'
    }]
)
print(response.content[0].text)
"
        ;;
    
    "review")
        if [ -z "$2" ]; then
            echo "Usage: claude-cli.sh review <file>"
            exit 1
        fi
        
        python3 claude-code-reviewer.py "$2"
        ;;
    
    "test")
        if [ -z "$2" ]; then
            echo "Usage: claude-cli.sh test <file>"
            exit 1
        fi
        
        code_content=$(cat "$2")
        python3 -c "
import anthropic
client = anthropic.Anthropic(api_key='$CLAUDE_API_KEY')
response = client.messages.create(
    model='$CLAUDE_MODEL',
    max_tokens=2000,
    messages=[{
        'role': 'user', 
        'content': 'Generate comprehensive test cases for this code:\n\n$code_content'
    }]
)
print(response.content[0].text)
"
        ;;
    
    *)
        echo "Claude Code CLI Tool"
        echo "Usage:"
        echo "  claude-cli.sh generate <prompt>  - Generate code from prompt"
        echo "  claude-cli.sh explain <file>     - Explain code in file"
        echo "  claude-cli.sh review <file>      - Review code in file"
        echo "  claude-cli.sh test <file>        - Generate tests for code"
        echo ""
        echo "Environment variables:"
        echo "  ANTHROPIC_API_KEY - Your Anthropic API key"
        ;;
esac

Best Practices and Tips

Effective Prompting

python

# Best practices for Claude Code prompts

# Be specific about requirements
good_prompt = """
Create a Python function that:
- Validates email addresses using regex
- Returns True for valid emails, False for invalid
- Handles edge cases (empty strings, None values)
- Includes comprehensive docstring
- Uses type hints
- Includes at least 3 test cases
"""

# Provide context and constraints
contextual_prompt = """
I'm building a web scraping application. Create a Python class that:
- Uses requests and BeautifulSoup
- Implements rate limiting (max 1 request per second)
- Handles HTTP errors gracefully
- Supports custom headers and user agents
- Includes retry logic with exponential backoff
- Logs all activities
- Respects robots.txt

The class will be used to scrape product information from e-commerce sites.
"""

# Ask for explanations and alternatives
comprehensive_prompt = """
Implement a caching mechanism in Python that:
- Supports both in-memory and Redis backends
- Implements LRU eviction policy
- Includes TTL (time-to-live) support
- Is thread-safe
- Provides async/await support

Please also explain:
- When to use each backend
- Performance considerations
- Alternative caching strategies
- Potential pitfalls and how to avoid them
"""

Code Quality Guidelines

python

# Guidelines for working with Claude-generated code

def validate_generated_code(code: str) -> List[str]:
    """Validate Claude-generated code for common issues"""
    
    issues = []
    
    # Check for security issues
    security_patterns = [
        'eval(',
        'exec(',
        'os.system(',
        'subprocess.call(',
        'input(',  # In production code
        'raw_input('
    ]
    
    for pattern in security_patterns:
        if pattern in code:
            issues.append(f"Potential security issue: {pattern}")
    
    # Check for best practices
    if 'import *' in code:
        issues.append("Avoid wildcard imports")
    
    if 'except:' in code and 'except Exception:' not in code:
        issues.append("Use specific exception handling")
    
    # Check for documentation
    if 'def ' in code and '"""' not in code:
        issues.append("Missing docstrings for functions")
    
    return issues

def review_checklist():
    """Checklist for reviewing Claude-generated code"""
    
    checklist = [
        "□ Code follows language-specific style guidelines",
        "□ Proper error handling implemented",
        "□ Security considerations addressed",
        "□ Performance implications considered",
        "□ Code is testable and maintainable",
        "□ Documentation is comprehensive",
        "□ Dependencies are appropriate",
        "□ Edge cases are handled",
        "□ Code is compatible with target environment",
        "□ No hardcoded secrets or credentials"
    ]
    
    return checklist

Performance Optimization

python

# Tips for optimizing Claude Code usage

class ClaudeOptimizer:
    def __init__(self):
        self.cache = {}
        self.conversation_context = []
    
    def optimize_prompt_length(self, prompt: str, max_length: int = 4000) -> str:
        """Optimize prompt length for better performance"""
        
        if len(prompt) <= max_length:
            return prompt
        
        # Prioritize important sections
        sections = prompt.split('\n\n')
        essential_sections = []
        optional_sections = []
        
        for section in sections:
            if any(keyword in section.lower() for keyword in 
                   ['requirements', 'must', 'should', 'error', 'security']):
                essential_sections.append(section)
            else:
                optional_sections.append(section)
        
        # Build optimized prompt
        optimized = '\n\n'.join(essential_sections)
        
        # Add optional sections if space allows
        for section in optional_sections:
            if len(optimized) + len(section) + 2 <= max_length:
                optimized += '\n\n' + section
            else:
                break
        
        return optimized
    
    def batch_requests(self, prompts: List[str]) -> List[str]:
        """Batch multiple requests for efficiency"""
        
        # Combine related prompts
        combined_prompt = "Please address the following requests:\n\n"
        
        for i, prompt in enumerate(prompts, 1):
            combined_prompt += f"{i}. {prompt}\n\n"
        
        combined_prompt += "Please provide numbered responses for each request."
        
        return [combined_prompt]
    
    def use_conversation_context(self, new_prompt: str) -> str:
        """Leverage conversation context for better results"""
        
        if not self.conversation_context:
            return new_prompt
        
        context_summary = "Previous context:\n"
        for item in self.conversation_context[-3:]:  # Last 3 interactions
            context_summary += f"- {item}\n"
        
        return f"{context_summary}\nCurrent request:\n{new_prompt}"

Troubleshooting

Common Issues and Solutions

python

# Common issues when using Claude Code

def troubleshoot_api_issues():
    """Common API issues and solutions"""
    
    issues = {
        "Rate limiting": {
            "symptoms": ["429 status code", "Rate limit exceeded"],
            "solutions": [
                "Implement exponential backoff",
                "Reduce request frequency",
                "Use batch requests when possible",
                "Check your usage limits"
            ]
        },
        
        "Token limits": {
            "symptoms": ["Response truncated", "Max tokens reached"],
            "solutions": [
                "Reduce prompt length",
                "Split complex requests",
                "Increase max_tokens parameter",
                "Use conversation context efficiently"
            ]
        },
        
        "Authentication errors": {
            "symptoms": ["401 status code", "Invalid API key"],
            "solutions": [
                "Verify API key is correct",
                "Check environment variable",
                "Ensure API key has proper permissions",
                "Regenerate API key if needed"
            ]
        },
        
        "Poor code quality": {
            "symptoms": ["Buggy code", "Security issues", "Poor performance"],
            "solutions": [
                "Improve prompt specificity",
                "Provide better context",
                "Ask for explanations",
                "Request multiple alternatives",
                "Always review and test generated code"
            ]
        }
    }
    
    return issues

def debug_generated_code(code: str, error_message: str = None):
    """Debug issues with generated code"""
    
    debug_steps = [
        "1. Read the code carefully and understand its purpose",
        "2. Check for syntax errors and typos",
        "3. Verify all imports and dependencies",
        "4. Test with simple inputs first",
        "5. Add logging and debug prints",
        "6. Check error messages and stack traces",
        "7. Validate input parameters and types",
        "8. Review error handling logic",
        "9. Test edge cases and boundary conditions",
        "10. Ask Claude to explain or debug the code"
    ]
    
    if error_message:
        print(f"Error: {error_message}")
    
    print("Debug steps:")
    for step in debug_steps:
        print(f"  {step}")

Resources and Documentation

Official Resources

Community Resources

Integration Examples

This cheat sheet provides comprehensive guidance for using Claude Code as an AI-powered development assistant. Always validate and test AI-generated code before production use, and follow security best practices for API key management.

Claude Code AI Assistant Cheat Sheet ​

Overview ​

Getting Started ​

Web Interface Access ​

API Integration ​

IDE Integration Setup ​

Code Generation and Completion ​

Basic Code Generation ​

Advanced Code Generation ​

Example usage ​

Generate tests for the code ​

Save conversation ​

JavaScript/TypeScript Development ​

Go Development ​

Code Review and Analysis ​

Automated Code Review ​

Design Pattern Implementation ​

Example usage ​

VS Code Extension Integration ​

Command Line Tool ​

Best Practices and Tips ​

Effective Prompting ​

Code Quality Guidelines ​

Performance Optimization ​

Troubleshooting ​

Common Issues and Solutions ​

Resources and Documentation ​

Official Resources ​

Community Resources ​

Integration Examples ​

Claude Code AI Assistant Cheat Sheet

Overview

Getting Started

Web Interface Access

API Integration

IDE Integration Setup

Code Generation and Completion

Basic Code Generation

Advanced Code Generation

Example usage

Generate tests for the code

Save conversation

JavaScript/TypeScript Development

Go Development

Code Review and Analysis

Automated Code Review

Design Pattern Implementation

Example usage

VS Code Extension Integration

Command Line Tool

Best Practices and Tips

Effective Prompting

Code Quality Guidelines

Performance Optimization

Troubleshooting

Common Issues and Solutions

Resources and Documentation

Official Resources

Community Resources

Integration Examples