Python Error Handling

When to use this skill

Use Python Error Handling when an AI agent needs a reusable SKILL.md workflow for this job: 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.

When not to use it

Skip Python Error Handling when the task is outside the coding category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/python-error-handling/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/python-error-handling/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# 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

```python 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.

```python 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.

```python 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.

```python 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 Type | Exception | Example | |--------------|-----------|---------| | Invalid input | `ValueError` | Bad parameter values | | Wrong type | `TypeError` | Expected string, got int | | Missing item | `KeyError` | Dict key not found | | Operational failure | `RuntimeError` | Service unavailable | | Timeout | `TimeoutError` | Operation took too long | | File not found | `FileNotFoundError` | Path doesn't exist | | Permission denied | `PermissionError` | Access forbidden |

```python # 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

Intended uses

• 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