MCPInstall

python-anti-patterns

Use this skill when reviewing Python code for common anti-patterns to avoid. Use as a checklist when reviewing code, before finalizing implementations, or when debugging issues that might stem from known bad practices.

Installs
13.4K
Last 24h
+47
Trending rank
#3496
Security
Pass

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/wshobson/agents --skill python-anti-patterns
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads python-anti-patterns automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

Security audits

Independent scans of the skill's instructions and bundled files.

Gen Agent Trust Hub PassSAFE ·The skill is a comprehensive reference checklist covering common Python anti-patterns and their recommended fixes. It is text-only, educational, and contains no executable scripts or security risks.
Socket PassNo alerts
Snyk PassLOW ·Risk: LOW · No issues
Runlayer PassNONE ·1 file scanned · No issues
ZeroLeaks PassNONE ·Score: 93/100 · 2 sections analyzed

SKILL.md

The instructions your agent reads when the skill is triggered.

Python Anti-Patterns Checklist

A reference checklist of common mistakes and anti-patterns in Python code. Review this before finalizing implementations to catch issues early.

When to Use This Skill

  • Reviewing code before merge
  • Debugging mysterious issues
  • Teaching or learning Python best practices
  • Establishing team coding standards
  • Refactoring legacy code

Note: This skill focuses on what to avoid. For guidance on positive patterns and architecture, see the python-design-patterns skill.

Infrastructure Anti-Patterns

Scattered Timeout/Retry Logic

# BAD: Timeout logic duplicated everywhere
def fetch_user(user_id):
    try:
        return requests.get(url, timeout=30)
    except Timeout:
        logger.warning("Timeout fetching user")
        return None

def fetch_orders(user_id):
    try:
        return requests.get(url, timeout=30)
    except Timeout:
        logger.warning("Timeout fetching orders")
        return None

Fix: Centralize in decorators or client wrappers.

# GOOD: Centralized retry logic
@retry(stop=stop_after_attempt(3), wait=wait_exponential())
def http_get(url: str) -> Response:
    return requests.get(url, timeout=30)

Double Retry

# BAD: Retrying at multiple layers
@retry(max_attempts=3)  # Application retry
def call_service():
    return client.request()  # Client also has retry configured!

Fix: Retry at one layer only. Know your infrastructure's retry behavior.

Hard-Coded Configuration

# BAD: Secrets and config in code
DB_HOST = "prod-db.example.com"
API_KEY = "sk-12345"

def connect():
    return psycopg.connect(f"host={DB_HOST}...")

Fix: Use environment variables with typed settings.

# GOOD
from pydantic_settings import BaseSettings

class Settings(BaseSettings):
    db_host: str = Field(alias="DB_HOST")
    api_key: str = Field(alias="API_KEY")

settings = Settings()

Architecture Anti-Patterns

Exposed Internal Types

# BAD: Leaking ORM model to API
@app.get("/users/{id}")
def get_user(id: str) -> UserModel:  # SQLAlchemy model
    return db.query(UserModel).get(id)

Fix: Use DTOs/response models.

# GOOD
@app.get("/users/{id}")
def get_user(id: str) -> UserResponse:
    user = db.query(UserModel).get(id)
    return UserResponse.from_orm(user)

Mixed I/O and Business Logic

# BAD: SQL embedded in business logic
def calculate_discount(user_id: str) -> float:
    user = db.query("SELECT * FROM users WHERE id = ?", user_id)
    orders = db.query("SELECT * FROM orders WHERE user_id = ?", user_id)
    # Business logic mixed with data access
    if len(orders) > 10:
        return 0.15
    return 0.0

Fix: Repository pattern. Keep business logic pure.

# GOOD
def calculate_discount(user: User, orders: list[Order]) -> float:
    # Pure business logic, easily testable
    if len(orders) > 10:
        return 0.15
    return 0.0

Error Handling Anti-Patterns

Bare Exception Handling

# BAD: Swallowing all exceptions
try:
    process()
except Exception:
    pass  # Silent failure - bugs hidden forever

Fix: Catch specific exceptions. Log or handle appropriately.

# GOOD
try:
    process()
except ConnectionError as e:
    logger.warning("Connection failed, will retry", error=str(e))
    raise
except ValueError as e:
    logger.error("Invalid input", error=str(e))
    raise BadRequestError(str(e))

Ignored Partial Failures

# BAD: Stops on first error
def process_batch(items):
    results = []
    for item in items:
        result = process(item)  # Raises on error - batch aborted
        results.append(result)
    return results

Fix: Capture both successes and failures.

# GOOD
def process_batch(items) -> BatchResult:
    succeeded = {}
    failed = {}
    for idx, item in enumerate(items):
        try:
            succeeded[idx] = process(item)
        except Exception as e:
            failed[idx] = e
    return BatchResult(succeeded, failed)

Missing Input Validation

# BAD: No validation
def create_user(data: dict):
    return User(**data)  # Crashes deep in code on bad input

Fix: Validate early at API boundaries.

# GOOD
def create_user(data: dict) -> User:
    validated = CreateUserInput.model_validate(data)
    return User.from_input(validated)

Resource Anti-Patterns

Unclosed Resources

# BAD: File never closed
def read_file(path):
    f = open(path)
    return f.read()  # What if this raises?

Fix: Use context managers.

# GOOD
def read_file(path):
    with open(path) as f:
        return f.read()

Blocking in Async

# BAD: Blocks the entire event loop
async def fetch_data():
    time.sleep(1)  # Blocks everything!
    response = requests.get(url)  # Also blocks!

Fix: Use async-native libraries.

# GOOD
async def fetch_data():
    await asyncio.sleep(1)
    async with httpx.AsyncClient() as client:
        response = await client.get(url)

Type Safety Anti-Patterns

Missing Type Hints

# BAD: No types
def process(data):
    return data["value"] * 2

Fix: Annotate all public functions.

# GOOD
def process(data: dict[str, int]) -> int:
    return data["value"] * 2

Untyped Collections

# BAD: Generic list without type parameter
def get_users() -> list:
    ...

Fix: Use type parameters.

# GOOD
def get_users() -> list[User]:
    ...

Testing Anti-Patterns

Only Testing Happy Paths

# BAD: Only tests success case
def test_create_user():
    user = service.create_user(valid_data)
    assert user.id is not None

Fix: Test error conditions and edge cases.

# GOOD
def test_create_user_success():
    user = service.create_user(valid_data)
    assert user.id is not None

def test_create_user_invalid_email():
    with pytest.raises(ValueError, match="Invalid email"):
        service.create_user(invalid_email_data)

def test_create_user_duplicate_email():
    service.create_user(valid_data)
    with pytest.raises(ConflictError):
        service.create_user(valid_data)

Over-Mocking

# BAD: Mocking everything
def test_user_service():
    mock_repo = Mock()
    mock_cache = Mock()
    mock_logger = Mock()
    mock_metrics = Mock()
    # Test doesn't verify real behavior

Fix: Use integration tests for critical paths. Mock only external services.

Quick Review Checklist

Before finalizing code, verify:

  • No scattered timeout/retry logic (centralized)
  • No double retry (app + infrastructure)
  • No hard-coded configuration or secrets
  • No exposed internal types (ORM models, protobufs)
  • No mixed I/O and business logic
  • No bare except Exception: pass
  • No ignored partial failures in batches
  • No missing input validation
  • No unclosed resources (using context managers)
  • No blocking calls in async code
  • All public functions have type hints
  • Collections have type parameters
  • Error paths are tested
  • Edge cases are covered

Common Fixes Summary

Anti-PatternFix
Scattered retry logicCentralized decorators
Hard-coded configEnvironment variables + pydantic-settings
Exposed ORM modelsDTO/response schemas
Mixed I/O + logicRepository pattern
Bare exceptCatch specific exceptions
Batch stops on errorReturn BatchResult with successes/failures
No validationValidate at boundaries with Pydantic
Unclosed resourcesContext managers
Blocking in asyncAsync-native libraries
Missing typesType annotations on all public APIs
Only happy path testsTest errors and edge cases

FAQ

What does the python-anti-patterns skill do?

Use this skill when reviewing Python code for common anti-patterns to avoid. Use as a checklist when reviewing code, before finalizing implementations, or when debugging issues that might stem from known bad practices.

How do I install python-anti-patterns?

Run npx skills add https://github.com/wshobson/agents --skill python-anti-patterns in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/python-anti-patterns/ (personal) or .claude/skills/python-anti-patterns/ (project).

Which AI agents can use python-anti-patterns?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is python-anti-patterns safe to install?

Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer passed; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.

Who maintains python-anti-patterns?

It is published by wshobson in wshobson/agents. It has 13,406 recorded installs.

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