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name: python-simplifier description: Simplify overly complex Python code. Use when user asks to simplify, refactor, clean up, make more readable, reduce complexity, improve code quality, find code smells, detect duplicates, or analyze coupling in Python code. Triggers on requests like "simplify this code", "this is too complex", "make this more readable", "refactor this", "clean this up", "find issues", "analyze this codebase", or when reviewing code that exhibits complexity anti-patterns. For Django-specific analysis, use the django-simplifier skill instead.

Python Code Simplifier

Transform complex, hard-to-maintain Python code into clean, readable, idiomatic solutions.

Analysis Scripts

Copy# Comprehensive analysis (runs all checks)
python scripts/analyze_all.py /path/to/project

# Individual analyzers:
python scripts/analyze_complexity.py .       # Cyclomatic/cognitive complexity
python scripts/find_code_smells.py .         # Mutable defaults, bare excepts, etc.
python scripts/find_overengineering.py .     # YAGNI violations, unused abstractions
python scripts/find_dead_code.py .           # Unused imports, functions, variables
python scripts/find_unpythonic.py .          # Non-idiomatic patterns
python scripts/find_coupling_issues.py .     # Feature envy, low cohesion
python scripts/find_duplicates.py .          # Structural duplicate detection

# JSON output for CI/tooling
python scripts/analyze_all.py . --format json > report.json

Workflow

1. Analyze: Run analyze_all.py to identify all issues
2. Prioritize: Address high-severity issues (🔴) first
3. Simplify: Apply patterns below incrementally
4. Verify: Ensure simplified code is functionally equivalent

Simplification Principles

1. YAGNI: Don't add abstractions until needed
2. Preserve behavior: Simplification ≠ changing functionality
3. One change at a time: Incremental is safer
4. Readability over cleverness: Clear beats "smart"
5. Keep related code together: Locality matters

Common Simplification Patterns

Extract and Name

Copy# Before: Complex inline condition
if user.age >= 18 and user.country in ALLOWED and not user.banned:

# After: Named condition
is_eligible = user.age >= 18 and user.country in ALLOWED and not user.banned
if is_eligible:

Early Returns

Copy# Before: Deep nesting
def process(data):
    if data:
        if data.valid:
            if data.ready:
                return compute(data)
    return None

# After: Guard clauses
def process(data):
    if not data or not data.valid or not data.ready:
        return None
    return compute(data)

Comprehensions

Copy# Before: Manual loop
result = []
for item in items:
    if item.active:
        result.append(item.name)

# After: List comprehension
result = [item.name for item in items if item.active]

Dictionary Techniques

Copy# Before: Verbose key checking
if key in d:
    value = d[key]
else:
    value = default

# After: get() with default
value = d.get(key, default)

# Before: Manual grouping
groups = {}
for item in items:
    if item.category not in groups:
        groups[item.category] = []
    groups[item.category].append(item)

# After: defaultdict
from collections import defaultdict
groups = defaultdict(list)
for item in items:
    groups[item.category].append(item)

Context Managers

Copy# Before: Manual cleanup
f = open('file.txt')
try:
    data = f.read()
finally:
    f.close()

# After: with statement
with open('file.txt') as f:
    data = f.read()

Over-Engineering Anti-Patterns

Code Smells Quick Reference

Script Reference

When NOT to Simplify

• Working legacy code with no tests
• Performance-critical hot paths (measure first)
• Code that will be replaced soon
• External API constraints requiring complexity