python-error-handling
Structured input validation, exception design, and graceful failure handling for Python applications.
- Covers fail-fast validation patterns, meaningful exception hierarchies, and partial failure handling for batch operations
- Includes Pydantic integration for complex input validation with automatic error messages and custom exception types with context
- Demonstrates exception chaining to preserve debug trails, batch processing with per-item error tracking, and progress reporting for long operations
- Provides guidance on mapping failures to standard Python exceptions and converting external data to domain types at system boundaries
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.
More from wshobson/agents
tailwind-design-system
Build scalable design systems with Tailwind CSS v4, design tokens, component libraries, and responsive patterns. Use when creating component libraries, implementing design systems, or standardizing UI patterns.
41.0Ktypescript-advanced-types
Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applications. Use when implementing complex type logic, creating reusable type utilities, or ensuring compile-time type safety in TypeScript projects.
40.4Knodejs-backend-patterns
Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when creating Node.js servers, REST APIs, GraphQL backends, or microservices architectures.
31.8Kpython-performance-optimization
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
22.1Kapi-design-principles
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.
20.3Kpython-testing-patterns
Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.
19.7K