smellcheck 0.3.0

Python code smell detector -- 82 refactoring patterns, 55 AST checks, zero dependencies

smellcheck

Python Code Smell Detector & Refactoring Guide
82 refactoring patterns · 55 automated AST checks · zero dependencies

smellcheck is a Python code smell detector and refactoring catalog. It works as a pip-installable CLI, GitHub Action, pre-commit hook, or Agent Skills plugin for AI coding assistants.

No dependencies. Pure Python stdlib (ast, pathlib, json). Runs anywhere Python 3.10+ runs.

What are code smells? Code smells are surface-level patterns in source code that hint at deeper design problems — not bugs, but structural weaknesses that make code harder to maintain, extend, or understand. Learn more →

Installation

pip

pip install smellcheck

smellcheck src/
smellcheck myfile.py --format json
smellcheck src/ --min-severity warning --fail-on warning

GitHub Action

- uses: cheickmec/smellcheck@v1
  with:
    paths: 'src/'
    fail-on: 'error'       # exit 1 on error-level findings (default)
    min-severity: 'info'   # display all findings (default)
    format: 'github'       # GitHub annotations (default)

pre-commit

repos:
  - repo: https://github.com/cheickmec/smellcheck
    rev: v0.2.0
    hooks:
      - id: smellcheck
        args: ['--fail-on', 'warning']

Agent Skills (Claude Code, Codex CLI, Cursor, Copilot, Roo Code, Gemini CLI)

Any tool supporting the Agent Skills standard can install directly:

# Claude Code
/plugin marketplace add cheickmec/smellcheck
/plugin install python-refactoring@smellcheck

# OpenAI Codex CLI
$skill-installer install cheickmec/smellcheck

# Cursor
# Import from GitHub URL in skills settings

# Or install from a local clone
git clone https://github.com/cheickmec/smellcheck.git
# Point your tool to plugins/python-refactoring/skills/python-refactoring/

Manual setup for other tools

For tools without Agent Skills support (Aider, Windsurf, Continue.dev, Amazon Q), copy the relevant files into your project's instruction directory:

• Copy SKILL.md content into your tool's instruction file (.cursorrules, CONVENTIONS.md, .windsurf/rules/, etc.)
• Install with pip install smellcheck and run the smellcheck CLI

Usage

# Scan a directory
smellcheck src/

# Scan multiple files
smellcheck file1.py file2.py

# JSON output
smellcheck src/ --format json

# GitHub Actions annotations
smellcheck src/ --format github

# SARIF output (for GitHub Code Scanning)
smellcheck src/ --format sarif > results.sarif

# Filter by severity
smellcheck src/ --min-severity warning

# Control exit code
smellcheck src/ --fail-on warning   # exit 1 on warning or error
smellcheck src/ --fail-on info      # exit 1 on any finding

# Run only specific checks
smellcheck src/ --select SC101,SC701,SC210

# Skip specific checks
smellcheck src/ --ignore SC601,SC202

# Module execution
python3 -m smellcheck src/

# Generate a baseline of current findings
smellcheck src/ --generate-baseline > .smellcheck-baseline.json

# Only report findings not in the baseline
smellcheck src/ --baseline .smellcheck-baseline.json

Configuration

smellcheck reads [tool.smellcheck] from the nearest pyproject.toml:

[tool.smellcheck]
select = ["SC101", "SC201", "SC701"]  # only run these checks (default: all)
ignore = ["SC601", "SC202"]          # skip these checks
per-file-ignores = {"tests/*" = ["SC201", "SC206"]}  # per-path overrides
fail-on = "warning"                  # override default fail-on
format = "text"                      # override default format
baseline = ".smellcheck-baseline.json"  # suppress known findings

CLI flags override config values.

Inline Suppression

Add # noqa: SC701 to a line to suppress that check on that line:

def foo(x=[]):  # noqa: SC701
    return x

Use # noqa (no codes) to suppress all findings on that line. Multiple codes: # noqa: SC601,SC202

Baseline

For large codebases, you can adopt smellcheck incrementally using a baseline file. The baseline records fingerprints of existing findings so only new issues are reported.

# 1. Generate a baseline from the current state
smellcheck src/ --generate-baseline > .smellcheck-baseline.json

# 2. Run with the baseline — only new findings are reported
smellcheck src/ --baseline .smellcheck-baseline.json

# 3. Or set it in pyproject.toml so every run uses it automatically

Fingerprints are resilient to line-number changes — renaming or moving code around won't break the baseline. When you fix a baselined smell, its entry is silently ignored.

--generate-baseline and --baseline are mutually exclusive.

Features

• 55 automated smell checks -- per-file AST analysis, cross-file dependency analysis, and OO metrics
• 82 refactoring patterns -- numbered catalog with before/after examples, trade-offs, and severity levels
• Zero dependencies -- stdlib-only, runs on any Python 3.10+ installation
• Multiple output formats -- text (terminal), JSON (machine-readable), GitHub annotations (CI), SARIF 2.1.0 (Code Scanning)
• Configurable -- pyproject.toml config, inline suppression, CLI overrides
• Baseline support -- adopt incrementally by suppressing existing findings and only failing on new ones
• Four distribution channels -- pip, GitHub Action, pre-commit, Agent Skills

SARIF / Code Scanning

Upload smellcheck findings to GitHub Code Scanning so they appear as native alerts in the Security tab and as PR annotations:

# Add to your CI workflow
code-scanning:
  runs-on: ubuntu-latest
  permissions:
    security-events: write
  steps:
    - uses: actions/checkout@v4
    - uses: actions/setup-python@v5
      with:
        python-version: '3.12'
    - run: pip install smellcheck
    - run: smellcheck src/ --format sarif --min-severity warning > results.sarif
      continue-on-error: true
    - uses: github/codeql-action/upload-sarif@v4
      with:
        sarif_file: results.sarif
      if: always()

Results include stable fingerprints for deduplication across runs.

Detected Patterns

Every rule is identified by an SC code (e.g. SC701). Use SC codes in --select, --ignore, and # noqa comments.

Per-File (41 checks)

SC Code	Pattern	Severity
SC101	Setters (half-built objects)	warning
SC102	UPPER_CASE without Final	info
SC103	Unprotected public attributes	info
SC104	Half-built objects (init assigns None)	warning
SC105	Boolean flag parameters	info
SC106	Global mutable state	info
SC107	Sequential IDs	info
SC201	Long functions (>20 lines)	warning
SC202	Generic names (data, result, tmp)	info
SC203	input() in business logic	warning
SC204	Functions returning None or list	info
SC205	Excessive decorators (>3)	info
SC206	Too many parameters (>5)	warning
SC207	CQS violation (query + modify)	info
SC208	Unused function parameters	warning
SC209	Long lambda (>60 chars)	info
SC210	Cyclomatic complexity (>10)	warning
SC301	Extract class (too many methods)	info
SC302	isinstance chains	warning
SC303	Singleton pattern	warning
SC304	Dataclass candidate	info
SC305	Sequential tuple indexing	info
SC306	Lazy class (<2 methods)	info
SC307	Temporary fields	warning
SC401	Dead code after return	warning
SC402	Deep nesting (>4 levels)	warning
SC403	Loop + append pattern	info
SC404	Complex boolean expressions	warning
SC405	Boolean control flag in loop	info
SC406	Complex comprehension (>2 generators)	info
SC407	Missing default else branch	info
SC501	Error codes instead of exceptions	warning
SC502	Law of Demeter violation	info
SC601	Magic numbers	info
SC602	Bare except / unused exception variable	error
SC603	String concatenation for multiline	info
SC604	contextlib candidate	info
SC605	Empty catch block	warning
SC701	Mutable default arguments	error
SC702	open() without context manager	warning
SC703	Blocking calls in async functions	warning

Cross-File (10 checks)

SC Code	Pattern	Description
SC211	Feature envy	Function accesses external attributes more than own
SC308	Deep inheritance	Inheritance depth >4
SC309	Wide hierarchy	>5 direct subclasses
SC503	Cyclic imports	DFS cycle detection
SC504	God modules	>500 lines or >30 top-level definitions
SC505	Shotgun surgery	Function called from >5 different files
SC506	Inappropriate intimacy	>3 bidirectional class references between files
SC507	Speculative generality	Abstract class with no concrete subclasses
SC508	Unstable dependency	Stable module depends on unstable module
SC606	Duplicate functions	AST-normalized hashing across files

OO Metrics (5 checks)

SC Code	Metric	Threshold
SC801	Lack of Cohesion of Methods	>0.8
SC802	Coupling Between Objects	>8
SC803	Excessive Fan-Out	>15
SC804	Response for a Class	>20
SC805	Middle Man (delegation ratio)	>50%

Refactoring Reference Files

Each pattern includes a description, before/after code examples, and trade-offs:

File	Patterns
state.md	Immutability, setters, attributes (SC101, SC102, SC103, SC104, SC105, 030)
functions.md	Extraction, naming, parameters, CQS (SC201, 010, 020, SC203, 027, SC206, 037, SC207, 050, 052, SC208, SC209)
types.md	Classes, reification, polymorphism, nulls (SC301, 012, SC302, 015, 019, 022, 023, SC204, 038, 044, 048, SC306, SC307, SC308, SC309)
control.md	Guards, pipelines, conditionals, phases (SC402, SC403, SC207, SC404, 046, 047, 049, 053, SC405, 056, SC406, SC407)
architecture.md	DI, singletons, exceptions, delegates (SC303, SC106, SC107, 035, 045, SC501, SC502, SC505, SC506, SC507, SC508)
hygiene.md	Constants, dead code, comments, style (SC601, SC602, 011, SC606, SC401, 025, 031, 032, SC205, SC603, SC605)
idioms.md	Context managers, generators, unpacking (SC701, SC702, 059, 060, SC304, SC305, SC604)
metrics.md	OO metrics: cohesion, coupling, fan-out, response, delegation (SC801, SC802, SC803, SC804, SC805)

Compatibility

Tool	Install Method	Status
pip	pip install smellcheck	Native support
GitHub Actions	uses: cheickmec/smellcheck@v1	Native support
pre-commit	.pre-commit-config.yaml	Native support
Claude Code	/plugin install	Native support
OpenAI Codex CLI	$skill-installer	Native support
Cursor	GitHub import / .cursor/skills/	Native support
GitHub Copilot	MCP gallery	Native support
Roo Code	.roo/ directory	Native support
Gemini CLI	Agent Skills	Native support
Windsurf	Copy to .windsurf/rules/	Manual
Aider	--read CONVENTIONS.md	Manual
Continue.dev	.continue/rules/	Manual
Amazon Q	.amazonq/rules/	Manual

How It Compares

Feature	smellcheck	PyExamine	SMART-Dal	Pyscent
Automated detections	55	49	31	11
Refactoring guidance	82 patterns	None	None	None
Dependencies	0 (stdlib)	pylint, radon	DesigniteJava	pylint, radon, cohesion
Python-specific idioms	Yes	No	No	No
Cross-file analysis	Yes	Limited	Yes	No
OO metrics	5	19	0	1
Distribution channels	4 (pip, GHA, pre-commit, Agent Skills)	1	1	1

Contributing

Contributions welcome. The core detector is src/smellcheck/detector.py -- add new checks by extending the SmellDetector AST visitor class and adding a cross-file analysis function if needed.

# Development setup
git clone https://github.com/cheickmec/smellcheck.git
cd smellcheck
pip install -e .
pip install pytest

# Run tests
pytest tests/ -v

# Self-check
smellcheck src/smellcheck/

License

MIT