clean-code

name	clean-code
description	Use when writing, reviewing, or refactoring code. Apply when naming variables or functions, structuring classes, handling errors, writing tests, or when code feels complex or hard to understand. Based on Robert C. Martin's Clean Code.

Clean Code

Overview

Clean code reads like well-written prose. Every name reveals intent. Every function tells a story. Every class has a single purpose. The goal isn't just working code—it's code that others can understand quickly, modify safely, and extend confidently.

"Clean code always looks like it was written by someone who cares." — Michael Feathers

"You know you are working on clean code when each routine turns out to be pretty much what you expected." — Ward Cunningham

The Boy Scout Rule: Leave the code cleaner than you found it. Every commit should improve quality, even if just slightly. Small improvements compound.

Chapter References

This skill provides an overview with quick references. For detailed guidance with examples, see the chapter files:

• chapters/names.md - Meaningful Names (intention-revealing, searchable, pronounceable)
• chapters/functions.md - Functions (small, do one thing, few arguments)
• chapters/comments.md - Comments (why to avoid, what's acceptable)
• chapters/objects-and-data.md - Objects and Data Structures (Law of Demeter, DTOs)
• chapters/error-handling.md - Error Handling (exceptions, null handling, Special Case Pattern)
• chapters/tests.md - Unit Tests (TDD, F.I.R.S.T., clean tests)
• chapters/classes.md - Classes (SRP, cohesion, OCP, DIP)
• smells-and-heuristics.md - Complete code smells reference (66 smells with explanations)

Quick Reference: Names

Names should reveal intent and be searchable.

Rule	Bad	Good
Reveal intent	d	elapsedTimeInDays
Avoid disinformation	accountList (not a List)	accounts
Make distinctions	a1, a2	source, destination
Pronounceable	genymdhms	generationTimestamp
Searchable	7	MAX_CLASSES_PER_STUDENT
Classes = nouns	Process	Customer, Account
Methods = verbs	data	postPayment(), save()

Avoid: Manager, Processor, Data, Info in class names—they hint at unclear responsibilities.

Key insight: If you need a comment to explain what a variable is, rename it instead.

Quick Reference: Functions

Size and Scope

• Ideal: 4-10 lines, rarely over 20
• Indent level: Never more than one or two
• Do one thing — if you can extract another function with a non-restating name, it's doing too much

Arguments

Count	Guidance
0	Best
1	Good
2	Acceptable
3+	Avoid—wrap in object

Flag arguments (booleans) are ugly. They proclaim the function does two things. Split it:

# Bad
def render(is_suite: bool): ...

# Good
def render_for_suite(): ...
def render_for_single_test(): ...

Key Rules

• Command Query Separation: Do something OR answer something, not both
• No side effects: If checkPassword() also initializes a session, it lies
• Prefer exceptions to error codes: Separates happy path from error handling
• Extract try/catch blocks: Error handling is one thing

Quick Reference: Comments

Comments are, at best, a necessary evil. The proper use of comments is to compensate for our failure to express ourselves in code.

Delete These Comments

• Redundant — restating what code says
• Journal/changelog — use git
• Commented-out code — an abomination, git remembers
• Noise — // default constructor, // increment i
• Closing brace — } // end if means too much nesting

Acceptable Comments

• Legal notices
• Explanation of intent (why, not what)
• Warning of consequences (// takes 30 minutes)
• TODO (but clean them up)
• Clarifying external library behavior

The Rule: When you feel the urge to comment, first try to refactor the code so the comment would be unnecessary.

Quick Reference: Error Handling

Error handling is important, but if it obscures logic, it's wrong.

Rule	Details
Use exceptions over return codes	Separates algorithm from error handling
Provide context	Include operation that failed and type of failure
Wrap third-party APIs	Minimizes dependencies, enables mocking
Use Special Case Pattern	Return object that handles special case (empty list, default values)
Don't return null	Creates work, invites NullPointerException
Don't pass null	Worse than returning null—forbid it by default

# Bad - null checks everywhere
if employees is not None:
    for e in employees:
        total += e.pay

# Good - return empty collection instead of null
for e in get_employees():  # Returns [] if none
    total += e.pay

Quick Reference: Classes

Single Responsibility Principle (SRP)

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

Tests:

• Can you derive a concise name? (Avoid Manager, Processor, Super)
• Can you describe it in 25 words without "if," "and," "or," "but"?

Cohesion

Methods should use the class's instance variables. When methods cluster around certain variables but not others, the class should be split.

Open-Closed Principle (OCP)

Classes should be open for extension but closed for modification. Add new behavior via subclassing, not modifying existing code.

Dependency Inversion Principle (DIP)

Depend on abstractions, not concrete details. Inject dependencies for testability.

# Bad - can't test without network
class Portfolio:
    def __init__(self):
        self.exchange = TokyoStockExchange()

# Good - injectable, testable
class Portfolio:
    def __init__(self, exchange: StockExchange):
        self.exchange = exchange

Quick Reference: Tests

The Three Laws of TDD

1. Don't write production code until you have a failing test
2. Don't write more test than sufficient to fail
3. Don't write more production code than sufficient to pass

F.I.R.S.T. Principles

• Fast — Run quickly so you run them often
• Independent — Don't depend on each other
• Repeatable — Same result in any environment
• Self-Validating — Boolean output (pass/fail)
• Timely — Written just before production code

Clean Tests

• Readability is paramount
• Use BUILD-OPERATE-CHECK pattern
• Create domain-specific testing language
• One concept per test (not necessarily one assert)

Warning: Test code is just as important as production code. If you let tests rot, your code will rot too.

Objects vs Data Structures

Concept	Hides	Exposes	Easy to add...
Objects	Data	Functions	New types
Data Structures	Nothing	Data	New functions

The idea that everything is an object is a myth. Sometimes you want simple data structures with procedures operating on them.

Law of Demeter

A method should only call methods of:

• The class itself
• Objects it creates
• Objects passed as arguments
• Objects held in instance variables

Don't call methods on objects returned by allowed functions (train wrecks):

# Bad
output_dir = ctxt.get_options().get_scratch_dir().get_absolute_path()

# Good - tell the object to do the work
bos = ctxt.create_scratch_file_stream(class_file_name)

The Most Critical Smells

From Chapter 17's comprehensive list, these are the most important:

G5: Duplication

The root of all evil in software. Every duplication is a missed abstraction opportunity:

• Identical code → extract to function
• Repeated switch/if-else → polymorphism
• Similar algorithms → Template Method or Strategy pattern

G30: Functions Should Do One Thing

If you can extract another function from it, the original was doing more than one thing.

N1: Choose Descriptive Names

Names are 90% of what makes code readable. Take time to choose wisely.

F1: Too Many Arguments

Zero is best, then one, two, three. More requires justification.

F3: Flag Arguments

Boolean parameters mean the function does two things. Split it.

G9: Dead Code

Code that isn't executed. Delete it—version control remembers.

G11: Inconsistency

If you do something one way, do all similar things the same way.

C5: Commented-Out Code

An abomination. Delete it immediately.

The Craft

"Writing clean code requires the disciplined use of a myriad little techniques applied through a painstakingly acquired sense of 'cleanliness.' The code-sense is the key."

Clean code isn't written by following rules mechanically. It comes from values that drive disciplines—caring about craft, respecting readers of your code, and taking pride in professional work.

How do you write clean code? First drafts are clumsy—long functions, nested loops, arbitrary names, duplication. You refine: break out functions, change names, eliminate duplication, shrink methods. Nobody writes clean code from the start.

Getting software to work and making it clean are different activities. Most of us have limited room in our heads, so we focus on getting code to work first. The problem is that too many of us think we are done once the program works. We fail to switch to organization and cleanliness. We move on to the next problem rather than going back and breaking overstuffed classes into decoupled units.

Don't. Go back. Clean it up. Leave it better than you found it.