agentleFS
phanijappsphanijapps/memex/.agents/skills/python-clean-code/SKILL.md

python-clean-code

Skill0 starsChanged 57 days agoMITView on GitHub

Use when writing, fixing, editing, reviewing, or refactoring any Python code. Enforces Robert Martin's complete Clean Code catalog—naming, functions, comments, DRY, and boundary conditions.

  • Installs packages

8 other projects use a version of this, 1 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 0 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 6 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    When reviewing code, identify violations by rule number (e.g., "G5 violation: duplicated logic").

    line 128
  2. Very specific:

    When fixing or editing code, report what was fixed (e.g., "Fixed: extracted magic number to SECONDS_PER_DAY (G25)").

    line 129

“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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)").

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.