Dfrabik Dev Portfolio Skills

Claude AI Skill Guide

Introduction

Claude is Anthropic's state-of-the-art large language model (LLM) providing intelligent, helpful, and safe conversational AI capabilities. Available through Claude.ai, Claude API, and integrated tools like Claude Code, Claude empowers developers and knowledge workers with advanced reasoning, coding assistance, content creation, and problem-solving capabilities across industries.

Key Differentiators

• Constitutional AI: Trained using reinforcement learning from human feedback (RLHF) with constitutional principles for safer, more aligned responses
• Long Context Window: Extended context (200K tokens) for processing large documents and codebases
• Multimodal Capabilities: Image understanding alongside text analysis
• Code Excellence: Expert-level coding across 40+ programming languages
• Reasoning & Analysis: Strong performance on complex reasoning and analytical tasks
• Ethical Framework: Built-in safety guardrails and responsible AI practices
• Tool Integration: Claude Code, browser automation, file operations, and custom integrations

Core Concepts

1. Claude Models

Available Models (as of February 2025)

Model Selection Guidelines

• Opus: Complex analysis, multi-step coding, extensive research
• Sonnet: Balanced tasks, most production use cases
• Haiku: Quick responses, simple queries, high-volume applications

2. Access Methods

Claude.ai (Web Interface)

• Interactive chat with Claude
• File uploads and analysis
• Image understanding
• Conversation history
• Claude Code for advanced coding tasks

Claude API (Programmatic Access)

• RESTful API for integration
• Multiple SDKs (Python, JavaScript, TypeScript)
• Streaming responses for real-time interaction
• Tool use and function calling

Claude Code (IDE Integration)

• VSCode extension
• File management and editing
• Git integration
• Terminal execution
• Automated task completion

Chrome Extension

• Side panel for quick queries
• Web page context
• Screenshot analysis

Claude for Software Development

Code Generation & Review

Generating Code from Specifications

# Prompt pattern for code generation
"""
Create a Python function that:
1. Takes a list of numbers as input
2. Filters out duplicates while preserving order
3. Returns the deduplicated list

Requirements:
- Use only standard library
- Add docstring with examples
- Include type hints
- Handle edge cases (empty list, None)
"""

# Expected output: Well-structured, production-ready code with tests

Code Review Patterns

When asking Claude to review code:
1. Provide the complete context
2. Specify what to focus on:
   - Security vulnerabilities
   - Performance issues
   - Code style and conventions
   - Testability
   - Readability and maintainability
3. Request specific improvements
4. Ask for refactoring suggestions

Debugging Complex Issues

Effective debugging prompt structure:
1. Describe the problem clearly
2. Include error messages and stack traces
3. Share relevant code sections
4. Mention what you've already tried
5. Specify the expected vs actual behavior
6. Include environment details if relevant

Architecture & Design Patterns

Designing System Architecture

When designing systems, ask Claude to:
1. Analyze existing codebase patterns
2. Recommend scalable architecture
3. Identify potential bottlenecks
4. Suggest technology choices
5. Create migration strategies
6. Document design decisions

Example prompt:
"I have a Node.js application with 100k daily users.
Currently using Express.js + MongoDB. What architectural
improvements would help with scalability, assuming 10x growth?"

Design Pattern Recognition

// Claude can identify and implement patterns:

// Factory Pattern Example
class VehicleFactory {
  static createVehicle(type) {
    switch(type) {
      case 'car': return new Car();
      case 'bike': return new Bike();
      case 'truck': return new Truck();
    }
  }
}

// Observer Pattern Example
class EventEmitter {
  constructor() {
    this.listeners = {};
  }

  on(event, callback) {
    if (!this.listeners[event]) {
      this.listeners[event] = [];
    }
    this.listeners[event].push(callback);
  }

  emit(event, data) {
    if (this.listeners[event]) {
      this.listeners[event].forEach(cb => cb(data));
    }
  }
}

Testing & Test-Driven Development

Test Generation

# Claude can generate comprehensive test suites

import pytest
from mymodule import calculate_total

class TestCalculateTotal:
    def test_basic_calculation(self):
        assert calculate_total([10, 20, 30]) == 60

    def test_empty_list(self):
        assert calculate_total([]) == 0

    def test_single_item(self):
        assert calculate_total([42]) == 42

    def test_negative_numbers(self):
        assert calculate_total([-10, 20, -5]) == 5

    def test_floats(self):
        assert calculate_total([10.5, 20.3]) == pytest.approx(30.8)

    def test_invalid_input_raises_error(self):
        with pytest.raises(TypeError):
            calculate_total("invalid")

Integration Test Design

Claude excels at designing test strategies:

1. Unit Tests
   - Test individual functions
   - Mock external dependencies
   - Cover edge cases

2. Integration Tests
   - Test module interactions
   - Use test database
   - Verify API contracts

3. E2E Tests
   - Test complete workflows
   - Use real environment
   - Validate user journeys

4. Performance Tests
   - Benchmark critical paths
   - Load testing
   - Memory profiling

Content Creation & Documentation

Technical Writing

Documentation Generation

Claude can transform code into clear documentation:

- API documentation from docstrings
- Architecture decision records (ADRs)
- User guides and tutorials
- README files and project overviews
- Migration guides
- Troubleshooting guides
- Performance tuning recommendations

Example Documentation Request

Prompt:
"Create comprehensive documentation for this authentication
system including:
1. Setup and configuration
2. API reference (endpoints, parameters, responses)
3. Common workflows with examples
4. Security best practices
5. Troubleshooting section
6. Migration guide from old system"

Educational Content

Tutorial & Course Creation

Claude can develop educational materials:

- Step-by-step tutorials
- Concept explanations
- Interactive exercises
- Challenge problems
- Real-world examples
- Best practices guides
- Cheat sheets and quick references

Writing & Communication

Content Types Claude Excels At

Data Analysis & Processing

CSV & Data File Analysis

Analyzing Data Files

When working with data, provide:
1. Sample of the data
2. Data structure/schema
3. Analysis questions
4. Output format preferences
5. Any data transformations needed

Example:
"I have a CSV with 100k customer records including:
- customer_id, name, email, signup_date, revenue
- Analyze: Which customer segments are most valuable?
- Output: Summary statistics and segment recommendations"

Research & Information Synthesis

Comprehensive Research

Claude can help with research by:
1. Synthesizing information from your descriptions
2. Identifying key concepts and relationships
3. Suggesting research directions
4. Creating literature reviews
5. Analyzing market trends
6. Comparing options and technologies

Best Practices for Using Claude

Prompt Engineering

Effective Prompt Structure

1. Context: Provide background information
2. Task: Clearly state what you want
3. Requirements: Specify constraints and preferences
4. Format: Describe desired output format
5. Examples: Provide examples when helpful

Good Prompt Example:

"I'm building a real-time chat application with React.
The app currently uses Redux, but I'm considering
switching to React Query for server state and Context API
for UI state.

Should I make this migration? Consider:
- Migration complexity
- Performance implications
- Team learning curve
- Long-term maintainability

Provide a comparison table and your recommendation with reasoning."

Iterative Refinement

Conversation Flow Best Practices

1. Start with broad request
2. Review initial response
3. Ask clarifying follow-ups
4. Request refinements
5. Drill down into details
6. Export final version

Example iteration:
1. "Create a state machine for user authentication"
2. "Add password reset flow"
3. "Include email verification"
4. "Add rate limiting for failed attempts"
5. "Generate TypeScript types"
6. "Create unit tests"

Working with Code

Code-Related Workflows

Refactoring

"Refactor this code to:
1. Improve readability
2. Reduce complexity
3. Follow [framework] best practices
4. Add error handling
5. Include unit tests
6. Add JSDoc comments"

Performance Optimization

"Analyze this function for performance issues:
[code]

Consider:
- Time complexity
- Memory usage
- Database queries
- API calls
- Caching opportunities"

Security Review

"Review this code for security vulnerabilities:
[code]

Check for:
- Injection attacks (SQL, XSS, etc.)
- Authentication/Authorization issues
- Sensitive data exposure
- OWASP top 10
- Cryptographic weaknesses"

Claude API Integration

Basic API Usage

Python SDK Example

import anthropic

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

message = client.messages.create(
    model="claude-opus-4-1-20250805",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Explain quantum computing"}
    ]
)

print(message.content[0].text)

JavaScript SDK Example

const Anthropic = require("@anthropic-ai/sdk");

const client = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});

const message = await client.messages.create({
  model: "claude-opus-4-1-20250805",
  max_tokens: 1024,
  messages: [
    { role: "user", content: "Explain quantum computing" },
  ],
});

console.log(message.content[0].text);

Tool Use (Function Calling)

Define Tools

tools = [
    {
        "name": "calculator",
        "description": "Performs arithmetic operations",
        "input_schema": {
            "type": "object",
            "properties": {
                "operation": {
                    "type": "string",
                    "enum": ["add", "subtract", "multiply", "divide"]
                },
                "a": {"type": "number"},
                "b": {"type": "number"}
            },
            "required": ["operation", "a", "b"]
        }
    },
    {
        "name": "get_weather",
        "description": "Gets current weather for a location",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"]
        }
    }
]

Handle Tool Calls

def process_tool_call(tool_name, tool_input):
    if tool_name == "calculator":
        op = tool_input["operation"]
        a, b = tool_input["a"], tool_input["b"]
        if op == "add":
            return a + b
        elif op == "subtract":
            return a - b
        # ... other operations
    elif tool_name == "get_weather":
        location = tool_input["location"]
        # Fetch weather data
        return f"Weather for {location}: ..."

# In the conversation loop:
message = client.messages.create(
    model="claude-opus-4-1-20250805",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "What's 2 + 2?"}]
)

if message.stop_reason == "tool_use":
    for content_block in message.content:
        if content_block.type == "tool_use":
            tool_name = content_block.name
            tool_input = content_block.input
            result = process_tool_call(tool_name, tool_input)
            # Add result back to conversation

Vision Capabilities

Analyzing Images

import base64

def analyze_image(image_path):
    with open(image_path, "rb") as image_file:
        image_data = base64.standard_b64encode(image_file.read()).decode("utf-8")

    message = client.messages.create(
        model="claude-opus-4-1-20250805",
        max_tokens=1024,
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "image",
                        "source": {
                            "type": "base64",
                            "media_type": "image/jpeg",
                            "data": image_data,
                        },
                    },
                    {
                        "type": "text",
                        "text": "Describe what you see in this image"
                    }
                ],
            }
        ],
    )

    return message.content[0].text

Real-World Applications

AI-Powered Code Assistant

Features

• Real-time code generation from descriptions
• Intelligent debugging and error fixing
• Automated refactoring suggestions
• Security vulnerability scanning
• Performance optimization recommendations
• Test generation and coverage analysis

Content & Knowledge Management

Use Cases

• Automated documentation generation
• Content translation and localization
• Summary generation from long documents
• FAQ creation from documentation
• Knowledge base construction

Customer Service Automation

Applications

• Intelligent chatbot responses
• Email classification and routing
• Customer sentiment analysis
• FAQ answering
• Support ticket summarization

Data Analysis & Reporting

Capabilities

• Data interpretation and insights
• Report generation and analysis
• Trend identification
• Anomaly detection explanation
• Business intelligence synthesis

Educational Platforms

Features

• Personalized learning content
• Concept explanation
• Exercise generation
• Progress tracking feedback
• Adaptive learning paths

Performance Optimization

Cost Management

API Usage Optimization

• Use appropriate model size (Haiku for simple tasks)
• Cache repeated context using prompt caching
• Batch requests for efficiency
• Monitor token usage
• Implement rate limiting

Prompt Caching Example

# Reuse expensive context (like large documents)
message = client.messages.create(
    model="claude-opus-4-1-20250805",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are a helpful assistant."
        },
        {
            "type": "text",
            "text": "[Large documentation or context]",
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[{"role": "user", "content": "Answer a question"}]
)

Latency Optimization

• Use Sonnet or Haiku for faster responses
• Implement streaming for real-time output
• Batch process requests
• Cache common queries
• Use appropriate context window size

# Streaming example for faster perceived response
with client.messages.stream(
    model="claude-opus-4-1-20250805",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Write a story"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Safety & Responsible Use

Claude Code Safety

Security Best Practices

• Never expose sensitive credentials in prompts
• Always review generated code before execution
• Use sandboxed environments for testing
• Implement proper access controls
• Audit Claude-generated changes

Ethical Guidelines

Responsible AI Usage

• Don't use Claude for deceptive purposes
• Respect intellectual property
• Protect user privacy
• Avoid generating harmful content
• Use appropriate model guardrails

Resources for Further Learning

Official Documentation

• Claude API Documentation
• Claude Models Overview
• Claude Code Documentation
• Prompt Engineering Guide

Learning Resources

• Anthropic Research Papers
• Blog posts on AI safety and alignment
• Community examples and case studies
• Developer Discord community
• GitHub repositories with Claude integrations

Tools & Resources

Popular Libraries

• Official SDKs (Python, JavaScript)
• LangChain integration
• LlamaIndex integration
• Vercel AI SDK
• Hugging Face integration

Community Projects

• Open-source tools using Claude API
• Example applications
• Integration tutorials
• Prompt libraries
• Best practices guides

Quick Reference

Model Comparison

Common Prompting Patterns

1. Role-Playing
"You are a senior software engineer with 20 years of experience..."

2. Few-Shot Learning
"Here are examples of good code:
[example 1]
[example 2]
Now, write code that follows this pattern..."

3. Chain-of-Thought
"Think step-by-step about how to solve this problem..."

4. Structured Output
"Respond in JSON format with keys: title, description, examples"

5. Iterative Refinement
"First draft: [request]
Then refine by: [specific improvements]"

API Rate Limits

• Token limits depend on model selection
• Rate limiting applies to prevent abuse
• Monitor usage in Anthropic console
• Implement exponential backoff for retries
• Consider usage patterns for scaling

Common Errors & Solutions

Integration Examples

With Node.js Express

const express = require('express');
const Anthropic = require('@anthropic-ai/sdk');

const app = express();
const client = new Anthropic();

app.post('/api/chat', async (req, res) => {
  const { message } = req.body;

  const response = await client.messages.create({
    model: "claude-opus-4-1-20250805",
    max_tokens: 1024,
    messages: [{ role: "user", content: message }]
  });

  res.json({ response: response.content[0].text });
});

With Python Flask

from flask import Flask, request, jsonify
import anthropic

app = Flask(__name__)
client = anthropic.Anthropic()

@app.route('/api/chat', methods=['POST'])
def chat():
    data = request.get_json()
    message = data.get('message')

    response = client.messages.create(
        model="claude-opus-4-1-20250805",
        max_tokens=1024,
        messages=[{"role": "user", "content": message}]
    )

    return jsonify({"response": response.content[0].text})

if __name__ == '__main__':
    app.run(debug=True)