Clean Code Skill

This skill embodies the principles of "Clean Code" by Robert C. Martin (Uncle Bob). Use it to transform "code that works" into "code that is clean."

🧠 Core Philosophy

"Code is clean if it can be read, and enhanced by a developer other than its original author." — Grady Booch

When to Use

Use this skill when:

• Writing new code: To ensure high quality from the start.
• Reviewing Pull Requests: To provide constructive, principle-based feedback.
• Refactoring legacy code: To identify and remove code smells.
• Improving team standards: To align on industry-standard best practices.

1. Meaningful Names

• Use Intention-Revealing Names: elapsedTimeInDays instead of d.
• Avoid Disinformation: Don't use accountList if it's actually a Map.
• Make Meaningful Distinctions: Avoid ProductData vs ProductInfo.
• Use Pronounceable/Searchable Names: Avoid genymdhms.
• Class Names: Use nouns (Customer, WikiPage). Avoid Manager, Data.
• Method Names: Use verbs (postPayment, deletePage).

2. Functions

• Small!: Functions should be shorter than you think.
• Do One Thing: A function should do only one thing, and do it well.
• One Level of Abstraction: Don't mix high-level business logic with low-level details (like regex).
• Descriptive Names: isPasswordValid is better than check.
• Arguments: 0 is ideal, 1-2 is okay, 3+ requires a very strong justification.
• No Side Effects: Functions shouldn't secretly change global state.

3. Comments

• Don't Comment Bad Code—Rewrite It: Most comments are a sign of failure to express ourselves in code.
• Explain Yourself in Code:

``python # Check if employee is eligible for full benefits if employee.flags & HOURLY and employee.age > 65: ` vs `python if employee.isEligibleForFullBenefits(): ``

• Good Comments: Legal, Informative (regex intent), Clarification (external libraries), TODOs.
• Bad Comments: Mumbling, Redundant, Misleading, Mandated, Noise, Position Markers.

4. Formatting

• The Newspaper Metaphor: High-level concepts at the top, details at the bottom.
• Vertical Density: Related lines should be close to each other.
• Distance: Variables should be declared near their usage.
• Indentation: Essential for structural readability.

5. Objects and Data Structures

• Data Abstraction: Hide the implementation behind interfaces.
• The Law of Demeter: A module should not know about the innards of the objects it manipulates. Avoid a.getB().getC().doSomething().
• Data Transfer Objects (DTO): Classes with public variables and no functions.

6. Error Handling

• Use Exceptions instead of Return Codes: Keeps logic clean.
• Write Try-Catch-Finally First: Defines the scope of the operation.
• Don't Return Null: It forces the caller to check for null every time.
• Don't Pass Null: Leads to NullPointerException.

7. Unit Tests

• The Three Laws of TDD:

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

• F.I.R.S.T. Principles: Fast, Independent, Repeatable, Self-Validating, Timely.

8. Classes

• Small!: Classes should have a single responsibility (SRP).
• The Stepdown Rule: We want the code to read like a top-down narrative.

9. Smells and Heuristics

• Rigidity: Hard to change.
• Fragility: Breaks in many places.
• Immobility: Hard to reuse.
• Viscosity: Hard to do the right thing.
• Needless Complexity/Repetition.

🛠️ Implementation Checklist

• Is this function smaller than 20 lines?
• Does this function do exactly one thing?
• Are all names searchable and intention-revealing?
• Have I avoided comments by making the code clearer?
• Am I passing too many arguments?
• Is there a failing test for this change?

Example

User request:

Refactor this working code for clearer names, smaller units, explicit errors, and preserved behavior; verify it with focused tests.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.