1# Context: E-commerce checkout system
2# Language: Python with Flask
3# Action: Create function to calculate total price
4# Requirements:
5#   - Apply discount code if valid
6#   - Add shipping fee based on location
7#   - Handle tax calculation (10%)
8# Example:
9#   Input: items=[{"price": 100, "qty": 2}], discount="SAVE10", location="VN"
10#   Output: {"subtotal": 200, "discount": 20, "shipping": 30, "tax": 21, "total": 231}
11
12def calculate_checkout_total(items, discount_code=None, location="US"):
13    # Copilot sẽ implement chính xác theo requirements!

1# TODO: Implement binary search algorithm
2# TODO: Add error handling for edge cases
3# TODO: Optimize for large datasets
4
5# FIXME: This function is too slow for large inputs
6# FIXME: Memory leak when processing big files
7
8# HACK: Temporary workaround for API rate limit

1# Step 1: Read the CSV file
2# Step 2: Clean the data (remove nulls, fix types)
3# Step 3: Calculate summary statistics
4# Step 4: Generate visualization
5# Step 5: Export results to Excel
6
7def analyze_sales_data(filepath):
8    # Step 1: Read the CSV file
9    df = pd.read_csv(filepath)
10    
11    # Step 2: Clean the data
12    # Copilot continues...

1# Input: List of student scores [{"name": str, "score": int}]
2# Output: Dictionary with statistics {"avg": float, "max": int, "min": int, "passed": int}
3# Note: Pass threshold is 50
4
5def calculate_class_statistics(students):
6    pass

1# Constraints:
2# - Time complexity must be O(n log n) or better
3# - Space complexity must be O(1)
4# - Must handle negative numbers
5# - Must not modify the original array
6
7def sort_array_efficiently(arr):
8    pass

1/explain this function step by step, including time complexity

1I'm getting this error: [paste error]
2The code is: [paste code]
3What's wrong and how to fix it?

1Refactor this code to:
2- Use list comprehension instead of loops
3- Add type hints
4- Follow PEP 8 style guide

1Create a Python class for User with:
2- Properties: name, email, created_at
3- Methods: validate_email(), to_dict(), __str__()
4- Use dataclass decorator

1# File: models/user.py
2# Related files: models/base.py, utils/validators.py
3# Database: PostgreSQL with SQLAlchemy
4
5from .base import BaseModel
6from utils.validators import validate_email
7
8class User(BaseModel):
9    # Copilot hiểu architecture từ context

1@workspace How is authentication implemented in this project?
2@workspace Find all API endpoints that require admin access
3@workspace What database models exist?

1# See also: utils/helpers.py for helper functions
2# Based on: docs/api-spec.md
3
4# Similar to: services/payment_service.py

1# Bad: Too vague
2# Process the data
3def process(data):
4    pass
5
6# Good: Specific
7# Parse JSON data, extract user info, validate email format
8def process_user_json(json_data):
9    pass

1# Bad: Contradictory
2# Sort ascending, then reverse to descending
3# Return first element which is largest
4
5# Good: Clear single goal
6# Return the largest element from the list

1# Bad: Too much at once
2# Build a complete REST API with user auth, CRUD for posts, 
3# comments, likes, real-time notifications, admin dashboard...
4
5# Good: Break down
6# Step 1: Create Flask app skeleton
7# Step 2: Add User model with basic fields
8# Step 3: Implement register endpoint

1# Use type hints - Copilot loves them!
2def merge_dicts(dict1: dict, dict2: dict) -> dict:
3    pass
4
5# Mention specific libraries
6# Using pandas DataFrame operations
7# Using requests library for API calls

1// TypeScript interfaces help a lot
2interface User {
3  id: number;
4  name: string;
5  email: string;
6}
7
8// Mention frameworks
9// React functional component with useState and useEffect
10// Express.js middleware for authentication

1-- Table schema: users(id, name, email, created_at)
2-- Table schema: orders(id, user_id, amount, status, created_at)
3
4-- Query: Get top 10 users by total order amount
5-- Include: user name, email, total amount, order count
6-- Filter: Only completed orders from last 30 days

1# Web scraper for news articles
2# Structure:
3# - NewsArticle dataclass
4# - Scraper class with methods
5# - Main function
6
7@dataclass
8class NewsArticle:
9    # Copilot generates fields

1# Add these fields: title, url, author, published_date, content
2@dataclass
3class NewsArticle:
4    # Now Copilot has specific fields

1class NewsScraper:
2    # Methods needed:
3    # - __init__(self, base_url)
4    # - fetch_page(self, url) -> BeautifulSoup
5    # - parse_article(self, soup) -> NewsArticle
6    # - scrape_all(self, max_pages=10) -> List[NewsArticle]

1"""
2Weather API Client
3
4This module provides a client for OpenWeatherMap API.
5
6Features:
7- Get current weather by city name
8- Get 5-day forecast
9- Get weather by coordinates
10- Cache results for 10 minutes to avoid rate limiting
11- Handle API errors gracefully
12
13Dependencies: requests, cachetools
14
15API Reference: https://openweathermap.org/api
16
17Example Usage:
18    client = WeatherClient(api_key="your_key")
19    current = client.get_current("Hanoi")
20    print(f"Temperature: {current.temperature}°C")
21    
22    forecast = client.get_forecast("Ho Chi Minh City", days=5)
23    for day in forecast:
24        print(f"{day.date}: {day.high}°C / {day.low}°C")
25"""
26
27from dataclasses import dataclass
28from typing import List, Optional
29from datetime import datetime
30import requests
31from cachetools import TTLCache
32
33# TODO: Let Copilot implement based on the documentation above
34
35@dataclass
36class Weather:
37    # Let Copilot suggest fields based on context
38    pass
39
40@dataclass  
41class Forecast:
42    # Let Copilot suggest fields
43    pass
44
45class WeatherClient:
46    # Let Copilot implement methods
47    pass