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python-error-handling

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Python error handling and debugging. Use this skill when implementing error handling, designing exception hierarchies, debugging issues, or adding logging. Covers exception patterns, custom exceptions, logging with structlog, debugging tools, and error recovery.

1 stars
1.2k downloads
Updated 2/2/2026

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SKILL.md

Python Error Handling

Modern error handling, logging, and debugging patterns.

Core Principles

  1. Specific exceptions - Catch specific types, not Exception
  2. Fail fast - Raise errors early at source
  3. Context in errors - Include relevant information
  4. Log, don't just print - Structured, searchable logs

Exception Handling Patterns

Be specific about what you catch

# BAD - Too broad
try:
    result = process_data()
except:
    print("Error occurred")

# GOOD - Specific handling
try:
    result = process_data()
except FileNotFoundError as e:
    logger.error(f"Config file not found: {e}")
    use_defaults()
except ValueError as e:
    logger.error(f"Invalid data: {e}")
    raise
except Exception as e:
    logger.error(f"Unexpected error: {e}", exc_info=True)
    raise

See exception-patterns.md.

Custom Exceptions

Create domain-specific exceptions

class DataValidationError(ValueError):
    """Raised when data validation fails."""
    pass

class APIError(Exception):
    """Base exception for API errors."""
    def __init__(self, message: str, status_code: int):
        self.message = message
        self.status_code = status_code
        super().__init__(self.message)

class APITimeout(APIError):
    """API request timeout."""
    def __init__(self, message: str):
        super().__init__(message, status_code=504)

See custom-exceptions.md.

Context Managers for Cleanup

from contextlib import contextmanager

@contextmanager
def managed_resource(name):
    resource = acquire_resource(name)
    try:
        yield resource
    finally:
        release_resource(resource)  # Always called

with managed_resource("database") as db:
    db.query("SELECT * FROM users")
    # Even if exception, resource released

Logging with Structlog

import structlog

logger = structlog.get_logger()

def process_order(order_id: str):
    log = logger.bind(order_id=order_id)
    
    try:
        log.info("processing_order_started")
        result = process(order_id)
        log.info("processing_order_completed", result=result)
        return result
    except Exception as e:
        log.error("processing_order_failed", error=str(e))
        raise

See logging-patterns.md.

Debugging Tools

Use pdb for interactive debugging

def complex_function(data):
    # Set breakpoint
    import pdb; pdb.set_trace()
    
    # Or in Python 3.7+
    breakpoint()
    
    result = process(data)
    return result

See debugging-tools.md.

Error Recovery Patterns

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=10)
)
def unreliable_api_call():
    """Retry with exponential backoff."""
    response = requests.get("https://api.example.com")
    response.raise_for_status()
    return response.json()

See retry-patterns.md.

source: Python error handling best practices

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AI Quality Score

95/100Analyzed 2/13/2026

A comprehensive and well-structured guide to Python error handling, covering exception patterns, custom exceptions, logging, debugging, and retries with clear, actionable code examples.

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Metadata

Licenseunknown
Version-
Updated2/2/2026
Publisherjustanesta

Tags

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