clean-code

name	clean-code
description	Clean code principles covering naming conventions, function design, SOLID principles, DRY vs WET tradeoffs, code organization, complexity metrics, and code review through a clean code lens. Use when the user asks about clean code, clean code best practices, or needs guidance on clean code implementation. Do NOT use when the user needs a different specialized skill or is asking about an unrelated technology domain.
license	Apache-2.0
metadata	{"author":"foundry-skills","version":"1.0.0","tags":"best-practices clean-code guide","category":"software-engineering","subcategory":"languages-runtimes","depends":"","disclaimer":"none","difficulty":"intermediate"}

Clean Code

You are an expert in clean code principles. Write code that is readable, maintainable, and intentional. Clean code reads like well-written prose. Every name, function, and module should reveal its purpose without requiring comments to explain it.

Naming Conventions

The Rules of Good Names

1. Names reveal intent. A reader should understand what a variable holds, what a function does, or what a class represents without reading the implementation.

# Bad
d = 7         # elapsed time in days
lst = []      # list of flagged accounts
temp = get()  # temporary result

# Good
elapsed_days = 7
flagged_accounts = []
active_user = get_authenticated_user()

2. Names are pronounceable. If you cannot say it in conversation, rename it.

// Bad
Date genymdhms;  // generation date, year-month-day-hour-minute-second
int pdcnt;       // past due count

// Good
Date generationTimestamp;
int pastDueCount;

3. Names are searchable. Single-letter names and magic numbers are invisible to search.

// Bad: searching for "7" finds thousands of results
if (days > 7) { ... }

// Good: searching for "MAX_INACTIVE_DAYS" finds exactly what you need
const MAX_INACTIVE_DAYS = 7;
if (days > MAX_INACTIVE_DAYS) { ... }

4. Avoid abbreviations unless universally understood (HTTP, URL, ID, DB).

5. Use consistent vocabulary. Pick one word per concept and stick with it. Do not use get, get, get, and load interchangeably in the same codebase.

Naming by Type

Type	Convention	Examples
Boolean	Phrase as question	isActive, hasPermission, canEdit, shouldRetry
Function	Verb + noun	calculateTotal, sendEmail, validateInput
Predicate function	is/has/can	isExpired(), hasAccess(), canProceed()
Collection	Plural noun	users, orderItems, activeConnections
Count	_count or num_	retryCount, numAttempts
Class	Noun	UserRepository, PaymentProcessor, OrderValidator
Interface	Adjective or noun	Serializable, Repository, EventHandler
Constant	UPPER_SNAKE_CASE	MAX_RETRIES, DEFAULT_TIMEOUT_MS

Naming Anti-Patterns

• Meaningless prefixes: IUserService, AbstractBaseFactory. Let the language features speak.
• Type in name: userList, nameString. The type system handles this.
• Noise words: data, info, manager, handler, processor. These add length without meaning.
• Negative booleans: isNotReady, disableFeature. Use positive names: isReady, featureEnabled.

Function Design

Functions Should Be Small

A function should do one thing, do it completely, and do it only. Target 5-15 lines. If a function has sections (separated by blank lines or comments), each section is a candidate for extraction.

Functions Should Have One Level of Abstraction

# Bad: mixed levels of abstraction
def process_order(order):
    # High-level
    validate_order(order)

    # Suddenly low-level
    conn = psycopg2.connect(host="db", port=5432, dbname="orders")
    cursor = conn.cursor()
    cursor.execute("INSERT INTO orders (id, total) VALUES (%s, %s)", (order.id, order.total))
    conn.commit()

    # Back to high-level
    send_confirmation(order)

# Good: consistent level of abstraction
def process_order(order):
    validate_order(order)
    save_order(order)
    send_confirmation(order)

Function Arguments

• 0 arguments (niladic): Best.
• 1 argument (monadic): Good. Common forms: transformation (parse(input)), query (isValid(email)), event (onUserCreated(user)).
• 2 arguments (dyadic): Acceptable. Ensure the order is intuitive (assertEquals(expected, actual)).
• 3 arguments (triadic): Should be rare. Consider introducing a parameter object.
• 4+ arguments: Refactor. Use a configuration object or builder.

Pure Functions

Prefer pure functions: same input always produces same output, no side effects.

# Pure: predictable, testable, parallelizable
def calculate_discount(price: float, discount_percent: float) -> float:
    return price * (1 - discount_percent / 100)

# Impure: depends on external state, has side effects
def apply_discount(order):
    discount = get_global_discount()  # external dependency
    order.total -= order.total * discount  # mutation
    log(f"Applied discount to {order.id}")  # side effect

Command-Query Separation

Functions should either do something (command) or answer something (query), not both.

// Bad: does it check or set?
boolean set(String attribute, String value);
if (set("username", "john")) { ... }

// Good: separate command and query
boolean attributeExists(String attribute);
void setAttribute(String attribute, String value);

if (attributeExists("username")) {
    setAttribute("username", "john");
}

SOLID Principles

S - Single Responsibility Principle

A class should have one, and only one, reason to change.

// Bad: UserService handles auth, validation, persistence, and notifications
class UserService {
  authenticate(credentials) { ... }
  validateEmail(email) { ... }
  saveToDatabase(user) { ... }
  sendWelcomeEmail(user) { ... }
}

// Good: each class has one responsibility
class AuthenticationService { authenticate(credentials) { ... } }
class UserValidator { validateEmail(email) { ... } }
class UserRepository { save(user) { ... } }
class NotificationService { sendWelcomeEmail(user) { ... } }

O - Open/Closed Principle

Software entities should be open for extension, closed for modification.

# Bad: adding a new shape requires modifying AreaCalculator
class AreaCalculator:
    def calculate(self, shape):
        if isinstance(shape, Circle):
            return math.pi * shape.radius ** 2
        elif isinstance(shape, Rectangle):
            return shape.width * shape.height
        # Must modify this class for every new shape

# Good: extend without modifying
class Shape(Protocol):
    def area(self) -> float: ...

class Circle:
    def __init__(self, radius): self.radius = radius
    def area(self) -> float: return math.pi * self.radius ** 2

class Rectangle:
    def __init__(self, width, height): self.width, self.height = width, height
    def area(self) -> float: return self.width * .height


 :
     (): .base, .height = base, height
     () -> :   * .base * .height

L - Liskov Substitution Principle

Objects of a superclass should be replaceable with objects of a subclass without altering program correctness.

# Violation: Square changes the behavior contract of Rectangle
class Rectangle:
    def set_width(self, w): self.width = w
    def set_height(self, h): self.height = h

class Square(Rectangle):
    def set_width(self, w): self.width = self.height = w   # Surprising!
    def set_height(self, h): self.width = self.height = h  # Surprising!

# Fix: use separate types or an immutable approach
class Shape(Protocol):
    def area(self) -> float: ...

class Rectangle:
    def __init__(self, width, height): ...
    def area(self): return self.width * self.height

class Square:
    def __init__(): ...
     ():  .side ** 

I - Interface Segregation Principle

Clients should not be forced to depend on interfaces they do not use.

// Bad: a printer-only device must implement fax and scan
interface Machine {
  print(doc: Document): void;
  fax(doc: Document): void;
  scan(doc: Document): Image;
}

// Good: segregated interfaces
interface Printer { print(doc: Document): void; }
interface Fax { fax(doc: Document): void; }
interface Scanner { scan(doc: Document): Image; }

class SimplePrinter implements Printer {
  print(doc: Document) { ... }
}

class MultiFunctionDevice implements Printer, Fax, Scanner {
  print(doc: Document) { ... }
  fax() { ... }
  () { ... }
}

D - Dependency Inversion Principle

High-level modules should not depend on low-level modules. Both should depend on abstractions.

# Bad: high-level OrderService depends on low-level MySQLDatabase
class OrderService:
    def __init__(self):
        self.db = MySQLDatabase()  # hard-coded dependency

# Good: depend on abstraction
class OrderService:
    def __init__(self, repository: OrderRepository):  # abstract dependency
        self.repository = repository

# Wire up at composition root
db = PostgresOrderRepository(connection_string)
service = OrderService(db)

DRY vs WET Tradeoffs

DRY (Don't Repeat Yourself)

Eliminate duplication of knowledge (not just code). If a business rule is expressed in two places, it will inevitably diverge.

When DRY Goes Wrong

# Over-DRY: shared utility for unrelated things
def format_thing(thing, type):
    if type == "user":
        return f"{thing.first_name} {thing.last_name}"
    elif type == "product":
        return f"{thing.name} - ${thing.price}"
    elif type == "order":
        return f"Order #{thing.id}"

This function couples three unrelated formatters. When user formatting changes, you risk breaking product formatting.

WET (Write Everything Twice) Rule

Allow duplication until you have 3+ instances. Then abstract. This prevents premature abstraction.

# Two similar functions: leave them separate
def validate_user_email(email): ...
def validate_contact_email(email): ...

# Third instance: now extract
def validate_email(email): ...

The Abstraction Test

Before extracting shared code, ask: If one caller needs a change, would ALL callers need the same change?

• Yes: Extract the shared code (real duplication).
• No: The similarity is coincidental. Keep separate (accidental duplication).

Code Organization

File Structure Principles

1. Group by feature, not by type (prefer user/controller.ts, user/model.ts over controllers/user.ts, models/user.ts).
2. Put related code close together. Functions that call each other should be in the same file or adjacent files.
3. Newspaper metaphor: High-level functions at the top, low-level details at the bottom. Readers scan top-down.
4. One concept per file. A file with 3 unrelated classes should be 3 files.

Vertical Formatting

• Caller above callee. A function should be defined below the function that calls it.
• Related concepts close together. Do not separate related functions with unrelated ones.
• Blank lines between concepts. Group related statements. Separate logical sections.

Complexity Metrics

Cyclomatic Complexity

Count the number of independent paths through a function.

def process(order):                           # +1 base
    if order.is_valid:                        # +1
        if order.total > 100:                 # +1
            apply_discount(order)
        elif order.is_member:                 # +1
            apply_member_discount(order)
        for item in order.items:              # +1
            if item.needs_shipping:           # +1
                schedule_shipping(item)
    else:
        raise InvalidOrderError()
# Cyclomatic complexity: 6

Targets:

• 1-5: Simple, low risk.
• 6-10: Moderate, consider refactoring.
• 11-20: Complex, refactor.
• 21+: Untestable. Refactor immediately.

Cognitive Complexity

Measures how hard code is to understand (Sonar metric). Penalizes nesting more heavily than branching.

Halstead Metrics

• Program length: Total number of operators and operands.
• Vocabulary: Number of distinct operators and operands.
• Difficulty: How error-prone the code is.

Clean Code Checklist for Review

When reviewing code through a clean code lens:

• Can I understand what each function does from its name alone?
• Are functions small (under 20 lines)?
• Does each function operate at one level of abstraction?
• Are there no commented-out code blocks?
• Are comments explaining "why", not "what"?
• Are magic numbers replaced with named constants?
• Is error handling clean (no empty catch blocks)?
• Are there no TODO comments older than 1 sprint?
• Does the code follow the project's naming conventions?
• Could a new team member understand this code without asking questions?
• Is the cyclomatic complexity of each function under 10?
• Is the code free of feature envy (method using another class more than its own)?

Comments

Good Comments

# Compensate for browser's non-standard handling of leap seconds
adjusted_time = timestamp + LEAP_SECOND_OFFSET

# WARNING: Order of operations matters. Tax must be calculated before discount
# because discounts are pre-tax per IRS regulation 26 CFR 1.61-1.
tax = calculate_tax(subtotal)
discount = calculate_discount(subtotal)

Bad Comments (Replace with Better Code)

# Bad: restating the code
i += 1  # increment i

# Bad: journal comments (use git log)
# 2024-01-15 John: Added validation
# 2024-01-20 Jane: Fixed edge case

# Bad: closing brace comments
if (condition) {
    ...
    ...
    ...
} // end if condition

# Bad: commented-out code (delete it, git remembers)
# old_result = legacy_calculate(x)
# if old_result != new_result:
#     log_discrepancy(old_result, new_result)

The Best Comment is No Comment

If you feel the need to comment, first try to express the same information through:

1. A better variable name.
2. A better function name.
3. Extracting a well-named function.
4. Using a well-named constant.

If the code still needs a comment after trying all four, write the comment. Explain why, not what.

When to Use

Use this skill when:

• Designing or implementing clean code solutions
• Reviewing or improving existing clean code approaches
• Making architectural or implementation decisions about clean code
• Learning clean code patterns and best practices
• Troubleshooting clean code-related issues

Do NOT use this skill when:

• The question is about a fundamentally different technology domain
• A more specific sibling skill covers the exact topic needed
• The user needs a complete hands-on tutorial rather than expert guidance

Output Format

# Clean Code Analysis

## Context Assessment
[Situation summary and constraints]

## Recommended Approach
[Primary recommendation with rationale]

## Implementation Steps
1. [Step with specific details]
2. [Step with specific details]
3. [Step with specific details]

## Trade-offs and Considerations
- [Key trade-off 1]
- [Key trade-off 2]

## Next Steps
- [Immediate action item]
- [Follow-up action item]

Example

Input: "Help me implement clean code for a medium-scale production application"

Output: A structured analysis covering current state assessment, recommended clean code approach with specific patterns, implementation roadmap with milestones, and risk mitigation strategies tailored to the application scale and constraints.

Edge Cases

• Legacy system integration: When clean code must coexist with legacy approaches, provide a gradual migration path rather than a complete rewrite
• Scale mismatch: When the solution complexity exceeds the project scale, recommend a simpler approach and note when to revisit
• Team skill gaps: When the team lacks experience with the recommended approach, include learning resources and simpler alternatives
• Conflicting requirements: When constraints conflict (e.g., performance vs. maintainability), explicitly state the trade-off and recommend based on stated priorities