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Python Error Handling

Seth HobsonCommunityCreated by Seth Hobson

  • Skill
  • Guidelines
  • Software development
  • MIT
  • No issues found

From the author

Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs.
— the description by Seth Hobson

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a comprehensive guide to building resilient Python applications. It focuses on defensive programming techniques, such as failing fast, using meaningful exception types, and implementing structured validation. By adopting these patterns, developers can create systems that are easier to debug, maintain, and operate in production environments, ensuring that errors are caught early and handled with appropriate context.

What it does

  • Defines patterns for early input validation at system boundaries.
  • Provides strategies for creating custom exception hierarchies.
  • Demonstrates how to handle partial failures in batch processing operations.
  • Shows how to use Pydantic for structured data validation.
  • Explains best practices for exception chaining to preserve debug trails.
  • Maps common failure types to standard Python built-in exceptions.

When to use it

  • When designing robust APIs that require strict input validation.
  • When building batch processing pipelines that must handle individual item failures.
  • When standardizing error reporting across a large application.
  • When you need to convert external data into typed domain objects.
  • When debugging complex systems where error context is frequently lost.

Advantages

  • Improves system reliability by catching errors before expensive operations.
  • Enhances debuggability through preserved exception context and clear error messages.
  • Reduces code duplication by standardizing validation and error handling logic.
  • Ensures consistent API behavior by enforcing strict input contracts.

Usage examples

“How do I validate a user input dictionary using Pydantic?”
The agent provides a Pydantic model example with field validators to ensure data integrity and structured error reporting.
“How can I process a list of items without stopping on the first error?”
The agent demonstrates a batch processing pattern that tracks successes and failures in a dictionary, allowing the caller to inspect individual results.
“What is the best way to re-raise an exception in Python?”
The agent explains the use of 'raise ... from e' to chain exceptions, preserving the original traceback for easier debugging.
“How should I handle API errors in Python?”
The agent provides a pattern for defining custom exception classes that carry status codes and response bodies for better error handling.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/wshobson/python-error-handling/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip python-error-handling.zip -d .agents/skills

Documentation by Seth Hobson

Python Error Handling

Build robust Python applications with proper input validation, meaningful exceptions, and graceful failure handling. Good error handling makes debugging easier and systems more reliable.

When to Use This Skill

  • Validating user input and API parameters

  • Designing exception hierarchies for applications

  • Handling partial failures in batch operations

  • Converting external data to domain types

  • Building user-friendly error messages

  • Implementing fail-fast validation patterns

Core Concepts

1. Fail Fast

Validate inputs early, before expensive operations. Report all validation errors at once when possible.

2. Meaningful Exceptions

Use appropriate exception types with context. Messages should explain what failed, why, and how to fix it.

3. Partial Failures

In batch operations, don't let one failure abort everything. Track successes and failures separately.

4. Preserve Context

Chain exceptions to maintain the full error trail for debugging.

Quick Start

def fetch_page(url: str, page_size: int) -> Page:
    if not url:
        raise ValueError("'url' is required")
    if not 1 <= page_size <= 100:
        raise ValueError(f"'page_size' must be 1-100, got {page_size}")
    # Now safe to proceed...

Fundamental Patterns

Pattern 1: Early Input Validation

Validate all inputs at API boundaries before any processing begins.

def process_order(
    order_id: str,
    quantity: int,
    discount_percent: float,
) -> OrderResult:
    """Process an order with validation."""
    # Validate required fields
    if not order_id:
        raise ValueError("'order_id' is required")

    # Validate ranges
    if quantity <= 0:
        raise ValueError(f"'quantity' must be positive, got {quantity}")

    if not 0 <= discount_percent <= 100:
        raise ValueError(
            f"'discount_percent' must be 0-100, got {discount_percent}"
        )

    # Validation passed, proceed with processing
    return _process_validated_order(order_id, quantity, discount_percent)
Pattern 2: Convert to Domain Types Early

Parse strings and external data into typed domain objects at system boundaries.

from enum import Enum

class OutputFormat(Enum):
    JSON = "json"
    CSV = "csv"
    PARQUET = "parquet"

def parse_output_format(value: str) -> OutputFormat:
    """Parse string to OutputFormat enum.

    Args:
        value: Format string from user input.

    Returns:
        Validated OutputFormat enum member.

    Raises:
        ValueError: If format is not recognized.
    """
    try:
        return OutputFormat(value.lower())
    except ValueError:
        valid_formats = [f.value for f in OutputFormat]
        raise ValueError(
            f"Invalid format '{value}'. "
            f"Valid options: {', '.join(valid_formats)}"
        )

# Usage at API boundary
def export_data(data: list[dict], format_str: str) -> bytes:
    output_format = parse_output_format(format_str)  # Fail fast
    # Rest of function uses typed OutputFormat
    ...
Pattern 3: Pydantic for Complex Validation

Use Pydantic models for structured input validation with automatic error messages.

from pydantic import BaseModel, Field, field_validator

class CreateUserInput(BaseModel):
    """Input model for user creation."""

    email: str = Field(..., min_length=5, max_length=255)
    name: str = Field(..., min_length=1, max_length=100)
    age: int = Field(ge=0, le=150)

    @field_validator("email")
    @classmethod
    def validate_email_format(cls, v: str) -> str:
        if "@" not in v or "." not in v.split("@")[-1]:
            raise ValueError("Invalid email format")
        return v.lower()

    @field_validator("name")
    @classmethod
    def normalize_name(cls, v: str) -> str:
        return v.strip().title()

# Usage
try:
    user_input = CreateUserInput(
        email="[email protected]",
        name="john doe",
        age=25,
    )
except ValidationError as e:
    # Pydantic provides detailed error information
    print(e.errors())
Pattern 4: Map Errors to Standard Exceptions

Use Python's built-in exception types appropriately, adding context as needed.

Failure TypeExceptionExample
Invalid inputValueErrorBad parameter values
Wrong typeTypeErrorExpected string, got int
Missing itemKeyErrorDict key not found
Operational failureRuntimeErrorService unavailable
TimeoutTimeoutErrorOperation took too long
File not foundFileNotFoundErrorPath doesn't exist
Permission deniedPermissionErrorAccess forbidden
# Good: Specific exception with context
raise ValueError(f"'page_size' must be 1-100, got {page_size}")

# Avoid: Generic exception, no context
raise Exception("Invalid parameter")

Detailed worked examples and patterns

Detailed sections (starting with ## Advanced Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.

Best Practices Summary

  1. Validate early - Check inputs before expensive operations

  2. Use specific exceptions - ValueError, TypeError, not generic Exception

  3. Include context - Messages should explain what, why, and how to fix

  4. Convert types at boundaries - Parse strings to enums/domain types early

  5. Chain exceptions - Use raise ... from e to preserve debug info

  6. Handle partial failures - Don't abort batches on single item errors

  7. Use Pydantic - For complex input validation with structured errors

  8. Document failure modes - Docstrings should list possible exceptions

  9. Log with context - Include IDs, counts, and other debugging info

  10. Test error paths - Verify exceptions are raised correctly

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

No issues found by our static scan· No issues found

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Attribution and licence

Created by
Seth Hobson
Publisher
Seth Hobson (Community)
Source
wshobson/agents · plugins/python-development/skills/python-error-handling · commit 4bde316
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Seth Hobson or Seth Hobson.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download python-error-handling.zip