Claude Code AI Assistant hoja de trucos
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.
⚠️ uso 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
# 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 documentación
# - Debugging assistance
# - Architecture recommendations
# - Code review and optimization
API Integration
# Install Anthropic Python SDK
pip install anthropic
# Basic API setup
impuerto anthropic
client = anthropic.Anthropic(
api_clave="your-api-clave-here"
)
# ejemplo 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
# VS Code Extension (unofficial community extensions)
# Search for "Claude" or "Anthropic" in VS Code marketplace
# Vim/Neovim integration
# Install via plugin manager (ejemplo 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 clave in settings
Code Generation and Completion
Basic Code Generation
# ejemplo prompts for code generation
# Function creation
prompt = """
Create a Python function that:
- Takes a list of dictionaries
- Filters by a specific clave-value pair
- Returns sorted results by another clave
- Includes error handling and type hints
"""
# Class implementation
prompt = """
Design a Python class for a REST API client that:
- Handles autenticación
- Implements retry logic
- Suppuertos async operations
- Includes comprehensive logging
"""
# algoritmo implementation
prompt = """
Implement a graph traversal algoritmo that:
- Uses breadth-first search
- Finds shortest path between nodes
- Handles weighted edges
- Returns path and total cost
"""
Advanced Code Generation
#!/usr/bin/env python3
# claude-code-generator.py
impuerto anthropic
impuerto json
impuerto os
from typing impuerto Dict, List, opciónal
class ClaudeCodeGenerator:
def __init__(self, api_clave: str):
self.client = anthropic.Anthropic(api_clave=api_clave)
self.conversation_history = []
def generate_code(self, prompt: str, language: str = "python",
context: opciónal[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: \\{error_message\\}
Code:
{code}
Please provide: 1. Root cause analysis 2. Specific fix recommendations 3. Corrected code 4. 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: 1. Performance improvements 2. Memory efficiency 3. Code readability 4. Maintainability 5. 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: 1. Unit tests for all functions/methods 2. Edge cases and boundary conditions 3. Error handling tests 4. Integration tests (if applicable) 5. Performance tests (if relevant) 6. 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\\\\}")
ejemplo uso
def main(): # Initialize with your API clave api_clave = os.getenv("ANTHROPIC_API_clave") if not api_clave: print("Please set ANTHROPIC_API_clave environment variable") return
generator = ClaudeCodeGenerator(api_clave)
# Generate a REST API client
prompt = """
Create a Python class for a REST API client that handles: - GET, POST, PUT, DELETE requests - autenticación with API claves - 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 Suppuerto
### Python Development
```python
# Python-specific prompts and ejemplos
# 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
- Expuertos results and models
"""
# Web development with Flask/Django
prompt = """
Build a Flask aplicación web that:
- Implements user autenticación
- 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 repuertoing
"""
JavaScript/TypeScript Development
// 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 autenticación
- 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
// Microservicios development
prompt := `
Create a Go microservicio 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
#!/usr/bin/env python3
# claude-code-reviewer.py
impuerto anthropic
impuerto os
impuerto subproceso
impuerto json
from pathlib impuerto Path
class ClaudeCodeReviewer:
def __init__(self, api_clave: str):
self.client = anthropic.Anthropic(api_clave=api_clave)
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: 1. Code quality and best practices 2. Security vulnerabilities 3. Performance issues 4. Maintainability concerns 5. documentación quality 6. Error handling 7. Testing coverage gaps 8. 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 = subproceso.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: 1. Code quality issues 2. Security vulnerabilities 3. Breaking changes 4. Performance implications 5. documentación updates needed 6. Testing requirements
Git diff:
{diff_content}
Focus on the changed lines and their Impacto 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 subproceso.CalledprocesoError as e:
return \\\\{
"error": f"Git comando 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_repuerto(self, reviews: List[Dict], output_file: str = "code_review_repuerto.md"):
"""Generate a comprehensive review repuerto"""
repuerto_lines = [
"# Code Review Repuerto",
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'])
repuerto_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':
repuerto_lines.extend([
f"### \\\\{review['file']\\\\}",
f"**Language**: \\\\{review['language']\\\\}",
"",
review['review'],
"",
"---",
""
])
elif review.get('status') == 'error':
repuerto_lines.extend([
f"### \\\\{review['file']\\\\} (ERROR)",
f"**Error**: \\\\{review['error']\\\\}",
"",
"---",
""
])
# Write repuerto
try:
with open(output_file, 'w', encoding='utf-8') as f:
f.write('\n'.join(repuerto_lines))
print(f"Review repuerto saved to: \\\\{output_file\\\\}")
except Exception as e:
print(f"Error saving repuerto: \\\\{e\\\\}")
def main(): impuerto sys
api_clave = os.getenv("ANTHROPIC_API_clave")
if not api_clave:
print("Please set ANTHROPIC_API_clave environment variable")
return
reviewer = ClaudeCodeReviewer(api_clave)
if len(sys.argv) < 2:
print("uso:")
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:
objetivo = sys.argv[1]
if os.path.isfile(objetivo):
# Review single file
review = reviewer.review_file(objetivo)
if review.get('status') == 'success':
print(f"Review for \\\\{objetivo\\\\}:")
print("=" * 50)
print(review['review'])
else:
print(f"Error: \\\\{review.get('error', 'Unknown error')\\\\}")
elif os.path.isdir(objetivo):
# Review directory
reviews = reviewer.review_directory(objetivo)
reviewer.generate_review_repuerto(reviews)
print(f"Reviewed \\\\{len(reviews)\\\\} files")
print("Detailed repuerto saved to: code_review_repuerto.md")
else:
print(f"Error: \\\\{objetivo\\\\} is not a valid file or directory")
if name == "main": main()
## Architecture and Design Assistance
### System Architecture Design
```python
# Architecture design prompts
# Microservicios architecture
prompt = """
Design a microservicios architecture for an e-commerce platform that includes:
- User management servicio
- Product catalog servicio
- Order procesoing servicio
- Payment servicio
- Notification servicio
- API gateway configuración
- Database design for each servicio
- Inter-servicio communication patterns
- Security and autenticación strategy
- Monitoring and logging approach
"""
# Database design
prompt = """
Design a database schema for a social media platform that suppuertos:
- User profiles and relationships
- Posts, comments, and reactions
- Real-time messaging
- Content moderation
- Analytics and repuertoing
- 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
- autenticación and autorización
- Rate limiting strategy
- Error handling patterns
- API versioning approach
- documentación structure
"""
Design Pattern Implementation
#!/usr/bin/env python3
# claude-design-patterns.py
impuerto anthropic
impuerto os
class ClaudeDesignPatternAssistant:
def __init__(self, api_clave: str):
self.client = anthropic.Anthropic(api_clave=api_clave)
def suggest_pattern(self, problem_Descripción: 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_Descripción\\\\}
objetivo Language: \\\\{language\\\\}
Please provide:
1. Recommended design patterns with explanations
2. Implementation ejemplos 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. uso ejemplos
3. Unit tests
4. documentación 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: 1. Identified code smells and issues 2. Recommended design patterns for improvement 3. Refactored code implementation 4. Explanation of improvements 5. 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\\\\}"
ejemplo uso
def main(): api_clave = os.getenv("ANTHROPIC_API_clave") if not api_clave: print("Please set ANTHROPIC_API_clave environment variable") return
assistant = ClaudeDesignPatternAssistant(api_clave)
# ejemplo: Suggest patterns for a logging system
problem = """
I need to design a logging system that: - Suppuertos multiple output destinations (file, console, database) - Allows different log levels (DEBUG, INFO, WARN, ERROR) - Can be configured at runtime - Suppuertos custom formatting - Is hilo-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_clave: $\\\\{\\\\{ secrets.ANTHROPIC_API_clave \\\\}\\\\}
run: |
python3 .github/scripts/claude-review.py
- name: Comment PR
uses: actions/github-script@v6
with:
script: |
const fs = require('fs');
const review = fs.readFileSync('claude-review-output.md', 'utf8');
github.rest.issues.createComment(\\\\{
issue_number: context.issue.number,
owner: context.repo.owner,
repo: context.repo.repo,
body: `## Claude Code Review\n\n$\\{review\\}`
\\\\});
VS Code Extension Integration
// VS Code settings for Claude integration
\\\\{
"claude.apiclave": "$\\\\{env:ANTHROPIC_API_clave\\\\}",
"claude.model": "claude-3-sonnet-20240229",
"claude.maxtokens": 1000,
"claude.autoComplete": true,
"claude.codeReview": true,
"claude.explainCode": true,
"claude.generateTests": true,
"claude.languages": [
"python",
"javascript",
"typescript",
"go",
"java",
"cpp"
]
\\\\}
comando Line Tool
#!/bin/bash
# claude-cli.sh - comando line interface for Claude Code
CLAUDE_API_clave="$\\\\{ANTHROPIC_API_clave\\\\}"
CLAUDE_MODEL="claude-3-sonnet-20240229"
if [ -z "$CLAUDE_API_clave" ]; then
echo "Error: ANTHROPIC_API_clave environment variable not set"
exit 1
fi
case "$1" in
"generate")
if [ -z "$2" ]; then
echo "uso: claude-cli.sh generate <prompt>"
exit 1
fi
python3 -c "
impuerto anthropic
client = anthropic.Anthropic(api_clave='$CLAUDE_API_clave')
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 "uso: claude-cli.sh explain <file>"
exit 1
fi
if [ ! -f "$2" ]; then
echo "Error: File $2 not found"
exit 1
fi
code_content=$(cat "$2")
python3 -c "
impuerto anthropic
client = anthropic.Anthropic(api_clave='$CLAUDE_API_clave')
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 "uso: claude-cli.sh review <file>"
exit 1
fi
python3 claude-code-reviewer.py "$2"
;;
"test")
if [ -z "$2" ]; then
echo "uso: claude-cli.sh test <file>"
exit 1
fi
code_content=$(cat "$2")
python3 -c "
impuerto anthropic
client = anthropic.Anthropic(api_clave='$CLAUDE_API_clave')
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 "uso:"
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_clave - Your Anthropic API clave"
;;
esac
Best Practices and Tips
Effective Prompting
# 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
- Suppuertos 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:
- Suppuertos both in-memory and Redis backends
- Implements LRU eviction policy
- Includes TTL (time-to-live) suppuerto
- Is hilo-safe
- Provides async/await suppuerto
Please also explain:
- When to use each backend
- Performance considerations
- Alternative caching strategies
- Potential pitfalls and how to avoid them
"""
Code Quality Guidelines
# 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(',
'subproceso.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 'impuerto *' in code:
issues.append("Avoid wildcard impuertos")
if 'except:' in code and 'except Exception:' not in code:
issues.append("Use specific exception handling")
# Check for documentación
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",
"□ documentación is comprehensive",
"□ Dependencies are appropriate",
"□ Edge cases are handled",
"□ Code is compatible with objetivo environment",
"□ No hardcoded secrets or credenciales"
]
return checklist
Performance Optimization
# Tips for optimizing Claude Code uso
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 impuertoant sections
sections = prompt.split('\n\n')
essential_sections = []
opciónal_sections = []
for section in sections:
if any(claveword in section.lower() for claveword in
['requirements', 'must', 'should', 'error', 'security']):
essential_sections.append(section)
else:
opciónal_sections.append(section)
# Build optimized prompt
optimized = '\n\n'.join(essential_sections)
# Add opciónal sections if space allows
for section in opciónal_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\\\\}"
solución de problemas
Common Issues and Solutions
# 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 uso limits"
]
\\\\},
"token limits": \\\\{
"symptoms": ["Response truncated", "Max tokens reached"],
"solutions": [
"Reduce prompt length",
"Split complex requests",
"Increase max_tokens parámetro",
"Use conversation context efficiently"
]
\\\\},
"autenticación errors": \\\\{
"symptoms": ["401 status code", "Invalid API clave"],
"solutions": [
"Verify API clave is correct",
"Check environment variable",
"Ensure API clave has proper permissions",
"Regenerate API clave 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 sintaxis errors and typos",
"3. Verify all impuertos and dependencies",
"4. Test with simple inputs first",
"5. Add logging and debug prints",
"6. Check error messages and stack traces",
"7. Validate input parámetros 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 documentación
Official Resources
Recursos de la Comunidad
Integration ejemplos
- VS Code Extension Development
- GitHub Actions Integration
- CI/CD Pipeline ejemplos
- API Integration Patterns
This hoja de trucos 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 clave management.