What problem does it solve? Python applications often fail unpredictably due to missing input validation, generic exceptions without context, and batch operations that abort entirely when a single item fails. This Skill provides concrete patterns for building robust error handling that makes debugging easier and systems more resilient. ## Core Features & Use Cases - Input Validation Patterns: Fail-fast validation at API boundaries, early conversion of strings to domain types like enums, and Pydantic models for structured validation with automatic error messages. - Exception Design: Custom exception hierarchies carrying structured context (status codes, retry delays), proper mapping to built-in exception types, and exception chaining with raise ... from e to preserve debug trails. - Partial Failure Handling: Batch processing patterns that track successes and failures per item instead of aborting, plus progress callbacks for long-running operations. - Use Case: When building an API endpoint that processes a batch of uploaded records, use these patterns to validate each record, return detailed per-item errors, and let valid records succeed even when some fail. ## Quick Start Show me how to validate function inputs and handle partial failures when processing a batch of records in Python.
