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python-clean-code

python-clean-code

github.com/ertugrul-dmrDevelopment208163 installsUpdated 27w ago
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About python-clean-code

python-clean-code is a Python code-review and editing skill based on Clean Code principles adapted from Robert C. Martin. It helps developers identify rule-specific issues and report fixes when changing code.

Key features

  1. 01Guidance across comments, functions, naming, tests, and design
  2. 02Python-specific rules for imports, enums, and type hints
  3. 03Violations identified by rule number
  4. 04Fix reports tied to rule numbers

Use cases

  1. 01Reviewing Python code for Clean Code violations
  2. 02Refactoring functions with excessive arguments or mixed responsibilities
  3. 03Checking tests, naming, and boundary conditions

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/ertugrul-dmr/clean-code-skills --skill python-clean-code

After installing, type/python-clean-codein Claude Code to confirm it loaded.

Details

From the project README

Clean Python: Complete Reference

Enforces all Clean Code principles from Robert C. Martin's Chapter 17, adapted for Python.

Comments (C1-C5)

  • C1: No metadata in comments (use Git)
  • C2: Delete obsolete comments immediately
  • C3: No redundant comments
  • C4: Write comments well if you must
  • C5: Never commit commented-out code

Environment (E1-E2)

  • E1: One command to build (pip install -e ".[dev]")
  • E2: One command to test (pytest)

Functions (F1-F4)

  • F1: Maximum 3 arguments (use dataclasses for more)
  • F2: No output arguments (return values)
  • F3: No flag arguments (split functions)
  • F4: Delete dead functions

General (G1-G36)

  • G1: One language per file
  • G2: Implement expected behavior
  • G3: Handle boundary conditions
  • G4: Don't override safeties
  • G5: DRY - no duplication
  • G6: Consistent abstraction levels
  • G7: Base classes don't know children
  • G8: Minimize public interface
  • G9: Delete dead code
  • G10: Variables near usage
  • G11: Be consistent
  • G12: Remove clutter
  • G13: No artificial coupling
  • G14: No feature envy
  • G15: No selector arguments
  • G16: No obscured intent
  • G17: Code where expected
  • G18: Prefer instance methods
  • G19: Use explanatory variables
  • G20: Function names say what they do
  • G21: Understand the algorithm
  • G22: Make dependencies physical
  • G23: Prefer polymorphism to if/else
  • G24: Follow conventions (PEP 8)
  • G25: Named constants, not magic numbers
  • G26: Be precise
  • G27: Structure over convention
  • G28: Encapsulate conditionals
  • G29: Avoid negative conditionals
  • G30: Functions do one thing
  • G31: Make temporal coupling explicit
  • G32: Don't be arbitrary
  • G33: Encapsulate boundary conditions
  • G34: One abstraction level per function
  • G35: Config at high levels
  • G36: Law of Demeter (no train wrecks)

Python-Specific (P1-P3)

These adapt the Java-specific rules (J1-J3) to Python conventions:

  • P1: No wildcard imports (from x import *) — opposite of Java, per PEP 8
  • P2: Use Enums, not magic constants — same principle as J3
  • P3: Type hints on public interfaces — Python's equivalent of Java's static typing

Names (N1-N7)

  • N1: Choose descriptive names
  • N2: Right abstraction level
  • N3: Use standard nomenclature
  • N4: Unambiguous names
  • N5: Name length matches scope
  • N6: No encodings
  • N7: Names describe side effects

Tests (T1-T9)

  • T1: Test everything that could break
  • T2: Use coverage tools
  • T3: Don't skip trivial tests
  • T4: Ignored test = ambiguity question
  • T5: Test boundary conditions
  • T6: Exhaustively test near bugs
  • T7: Look for patterns in failures
  • T8: Check coverage when debugging
  • T9: Tests must be fast (< 100ms each)

Quick Reference Table

CategoryRuleOne-Liner
CommentsC1No metadata (use Git)
C3No redundant comments
C5No commented-out code
FunctionsF1Max 3 arguments
F3No flag arguments
F4Delete dead functions
GeneralG5DRY—no duplication
G9Delete dead code
G16No obscured intent
G23Polymorphism over if/else
G25Named constants, not magic numbers
G30Functions do one thing
G36Law of Demeter (one dot)
NamesN1Descriptive names
N5Name length matches scope
TestsT5Test boundary conditions
T9Tests must be fast

Anti-Patterns (Don't → Do)

❌ Don't✅ Do
Comment every lineDelete obvious comments
Helper for one-linerInline the code
from x import *Explicit imports
Magic number 86400SECONDS_PER_DAY = 86400
process(data, True)process_verbose(data)
Deep nestingGuard clauses, early returns
obj.a.b.c.valueobj.get_value()
100+ line functionSplit by responsibility

AI Behavior

When reviewing code, identify violations by rule number (e.g., "G5 violation: duplicated logic"). When fixing or editing code, report what was fixed (e.g., "Fixed: extracted magic number to SECONDS_PER_DAY (G25)").

Comments

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