python-refactor

name

python-refactor

description

Systematic code refactoring skill that transforms complex, hard-to-understand code into clear, well-documented, maintainable code while preserving correctness. Applies structured refactoring patterns with validation. TRIGGER WHEN: users request "readable", "maintainable", or "clean" code, during code reviews flagging comprehension issues, for legacy code modernization, or in educational/onboarding contexts DO NOT TRIGGER WHEN: the task is outside the specific scope of this component.

Python Refactor

Transform complex Python into clear, maintainable code while preserving correctness. Phased workflow with safety-by-design and continuous validation. For deep references (anti-patterns, OOP principles, cognitive complexity, regression prevention), see the references/ directory.

When to invoke

• Explicit "human", "readable", "maintainable", "clean", or "refactor" request
• Code review flags comprehension or maintainability issues
• Legacy code modernization
• Onboarding / educational contexts
• Complexity metrics exceed thresholds
• Red flags: file > 500 lines with scattered functions and global state, multiple global statements, no clear module/class organization, configuration mixed with business logic

Do NOT invoke when

• Code is performance-critical and profiling shows perf optimization is needed first
• Code is scheduled for deletion or replacement
• External dependencies require upstream contributions instead
• User explicitly requested perf optimization over readability

Core principles (priority order)

1. Prefer structured OOP for complex code -- shared state, multiple concerns, scattered globals = restructure into classes/modules. (But: simple modules with pure functions, click/argparse CLIs, and functional pipelines DON'T need to be forced into classes.)
2. Clarity over cleverness -- explicit beats implicit
3. Preserve correctness -- all tests pass, behavior identical
4. Single Responsibility -- one thing per class/function (SOLID)
5. Self-documenting structure -- code = what, comments = why
6. Progressive disclosure -- reveal complexity in layers
7. Reasonable performance -- never sacrifice >2× without explicit approval

Hard constraints

• SAFETY BY DESIGN -- mandatory migration checklists for destructive changes. CREATE → SEARCH → MIGRATE → VERIFY → only then REMOVE. NEVER remove before 100% migration verified.
• STATIC ANALYSIS FIRST -- flake8 --select=F821,E0602 (or ruff check --select=F821) BEFORE tests. Catches NameErrors immediately.
• PRESERVE BEHAVIOR -- all existing tests pass after.
• NO PERF REGRESSION -- never degrade > 2× without explicit approval.
• NO API CHANGES -- public APIs unchanged unless explicitly requested + documented.
• NO OVER-ENGINEERING -- simple stays simple.
• NO MAGIC -- no framework magic, no metaprogramming unless absolutely necessary.
• VALIDATE CONTINUOUSLY -- static analysis + tests after each logical change.

Regression prevention (MANDATORY)

Refactoring must NEVER introduce regressions. Read references/REGRESSION_PREVENTION.md before any session.

Before each session:

• Test suite passes 100%
• Coverage ≥ 80% on target code (write tests FIRST if not)
• Golden outputs captured for critical edge cases
• Static analysis baseline saved

After EACH micro-change (not at the end -- every single one):

• flake8 --select=F821,E999 → 0 errors
• pytest -x → all passing
• Spot check 1 edge case for unchanged behavior

If ANY check fails: STOP → REVERT → ANALYZE → FIX APPROACH → RETRY.

ANY REGRESSION = TOTAL FAILURE.

Workflow (4 phases)

Phase 1: Analysis

1. Read the entire codebase section.
2. Identify readability issues using references/anti-patterns.md (script-like/global-state, God Objects, nested conditionals, long functions, magic numbers, cryptic names).
3. Assess architecture against references/oop_principles.md (proper classes/modules, encapsulated state, separated responsibilities, SOLID, DI vs hard-coded deps).
4. Measure current metrics with scripts/measure_complexity.py or scripts/analyze_multi_metrics.py.
5. Run linting analysis (see Tooling below).
6. Check test coverage; identify gaps to fill BEFORE refactoring.
7. Document with assets/templates/analysis_template.md.

Output: prioritized list of issues by impact and risk.

Phase 2: Planning

1. Classify each change:
   • Non-destructive (rename, docs, type hints) → low risk
   • Destructive (remove globals, delete functions, replace APIs) → high risk
2. For DESTRUCTIVE changes -- migration plan is MANDATORY:
   • Search ALL usages of each element to be removed
   • Document every usage (file, line, type)
   • No complete migration plan = cannot proceed with the destructive change
3. Risk assessment per change (Low/Medium/High)
4. Dependency map -- what depends on this code?
5. Test strategy -- what tests are needed? what might break?
6. Order changes safest → riskiest
7. Document expected metric improvements

Output: refactoring plan, sequenced changes, migration plans, test strategy, rollback plan.

Phase 3: Execution

Non-destructive (safe anytime)

1. Rename for clarity
2. Extract magic numbers/strings to named constants
3. Add/improve docs and type hints
4. Add guard clauses to reduce nesting

Destructive (STRICT PROTOCOL)

1. CREATE new structure (no removal) -- write new classes/functions + tests
2. SEARCH for ALL usages of the element being removed
3. CREATE migration checklist documenting every found usage
4. MIGRATE one usage at a time, checking off the list, running static analysis + tests after each
5. VERIFY complete migration -- re-run searches, should find zero old references
6. REMOVE old code only after 100% migration verified

Execution rules

• NEVER skip the migration checklist for destructive changes
• Run static analysis BEFORE tests
• One pattern at a time -- never mix multiple refactoring patterns in a single change
• Atomic commits -- each migration step gets its own commit
• Stop on ANY error (static analysis OR test failure) → immediate fix/revert

Recommended order

1. Transform script-like code to proper architecture (references/examples/script_to_oop_transformation.md)
2. Rename for clarity
3. Extract magic numbers/strings to constants/enums
4. Improve docs + type hints
5. Extract methods to reduce function length
6. Simplify conditionals with guard clauses
7. Reduce nesting depth
8. Final review: separation of concerns

Phase 4: Validation

1. Static analysis FIRST:

   flake8 <file> --select=F821,E0602          # undefined names/variables -- MUST be 0
   flake8 <file> --select=F401                # unused imports
   flake8 <file>                              # full quality check
   

2. Full test suite → 100% pass required.
3. Architecture validation: global state eliminated/encapsulated, proper modules/classes, separated responsibilities, SOLID compliance.
4. Before/after metrics with scripts/measure_complexity.py or scripts/analyze_multi_metrics.py.
5. Performance regression check with scripts/benchmark_changes.py for hot paths.
6. Summary report using assets/templates/summary_template.md.
7. Flag for human review if: perf degraded > 10%, public API signatures changed, test coverage decreased, significant architectural changes.

Refactoring patterns (catalog summary)

Full catalog with examples in references/patterns.md. Key patterns:

• Guard Clauses -- early returns instead of nested conditionals
• Extract Method -- split large functions into focused units (resets the nesting counter -- most powerful for cognitive complexity)
• Dictionary Dispatch -- replace if-elif chains with lookup tables
• Match Statement (Py 3.10+) -- counts as +1 total, not per branch
• Named Boolean Conditions -- extract complex booleans into named variables
• Encapsulate Global State -- move globals into classes with proper encapsulation
• Group Related Functions -- organize scattered functions into classes by responsibility
• Create Domain Models -- replace primitive dicts with dataclasses + enums
• Apply Dependency Injection -- replace hard-coded deps with injected ones

For cognitive complexity calculation rules and reduction strategies, see references/cognitive_complexity_guide.md.

Naming conventions

• Variables: descriptive, booleans as is_active / has_permission / can_edit, collections as plurals
• Functions: verb + object (calculate_total, validate_email); boolean queries as is_valid() / has_items()
• Constants: UPPERCASE_WITH_UNDERSCORES; replace magic numbers/strings
• Classes: PascalCase nouns (UserAccount, PaymentProcessor)

Anti-patterns to fix (priority order)

Full catalog: references/anti-patterns.md.

• Critical: script-like / procedural code with global state; God Object / God Class
• High: complex nested conditionals (> 3 levels), long functions (> 30 lines), magic numbers, cryptic names, missing type hints, missing docstrings
• Medium: duplicate code, primitive obsession, long parameter lists (> 5)
• Low: inconsistent naming, redundant comments, unused imports

Tooling

Primary stack: Ruff + Complexipy (recommended for new projects)

uv tool install ruff complexipy radon wily

ruff check src/                                 # fast linting (Rust, replaces flake8+plugins)
complexipy src/ --max-complexity-allowed 15     # cognitive complexity (Rust)
radon mi src/ -s                                # maintainability index

Full configuration (pyproject.toml, pre-commit, GitHub Actions): references/cognitive_complexity_guide.md.

Alternative: flake8 + curated plugins

For projects already on flake8, see references/flake8_plugins_guide.md (curated 16-plugin selector list).

Multi-metric analysis

scripts/analyze_multi_metrics.py combines complexipy + radon + maintainability index in a single report.

Metric	Tool	Use
Cognitive complexity	complexipy	Human comprehension
Cyclomatic complexity	ruff (C901), radon	Test planning
Maintainability index	radon	Overall code health

Metric targets

• Cyclomatic complexity: < 10 per function (warning 15, error 20)
• Cognitive complexity: < 15 per function (SonarQube default; warning 20)
• Function length: < 30 lines (warning 50)
• Nesting depth: ≤ 3 levels
• Docstring coverage: > 80% for public functions
• Type-hint coverage: > 90% for public APIs

Historical tracking with Wily

Trends matter, not just thresholds. Setup + CI integration: references/cognitive_complexity_guide.md.

Common refactoring mistakes

Full guide: references/REGRESSION_PREVENTION.md. Key traps:

1. Incomplete migration -- removing old code before ALL usages migrated (causes NameErrors).
2. Partial pattern application -- applying refactoring to some functions but not others.
3. Breaking public APIs -- changing signatures used by external code.
4. Assuming tests cover everything -- tests pass but runtime errors occur (run static analysis!).

When to reach for which tool

• clean-code (cross-language plugin) -- multi-language cosmetic cleanup; renames local vars, improves comments, simplifies structure. Lowest regression risk. Use for "make this readable", "clean up naming."
• python-refactor (this skill) -- Python-only deep restructuring. OOP transformation, SOLID, complexity metrics, migration checklists, benchmark validation. Use for "refactor this module", "reduce complexity", "transform to OOP."

Escalation path: clean-code → python-refactor (safest to most thorough).

Integration

• python-tdd -- set up tests before refactoring, validate coverage after
• python-performance-optimization -- deep profiling before/after
• python-packaging -- handle pyproject.toml + distribution if refactoring a library
• uv-package-manager -- uv run ruff, uv run complexipy for tool execution
• async-python-patterns -- reference async patterns when refactoring async code

When NOT to refactor

Perf-critical optimized code (profile first), code scheduled for deletion, external deps (contribute upstream), stable legacy code nobody needs to modify.

Limitations

Cannot improve algorithmic complexity (that's an algorithm change). Cannot add domain knowledge not in code/comments. Cannot guarantee correctness without tests. Style preferences vary -- adjust to team conventions.

Examples

references/examples/:

• script_to_oop_transformation.md -- script → clean OOP architecture (flagship case study)
• python_complexity_reduction.md -- nested conditionals and long functions
• typescript_naming_improvements.md -- naming patterns (cross-language reference)

Success criteria

1. Zero regressions -- all tests pass, behavior unchanged
2. Golden master match for documented critical cases
3. Complexity metrics improved (documented in summary)
4. No perf regression > 10% (or explicit approval)
5. Documentation coverage improved
6. Code easier for humans to understand
7. No new security vulnerabilities
8. Atomic, well-documented git history
9. Wily trend -- complexity not increased vs previous commit
10. Static analysis shows improvement