When should I use Result types instead of Exceptions?

The skill suggests using Result types for expected errors like validation failures, while reserving Exceptions for truly unexpected or exceptional runtime conditions.

What programming languages does this skill support?

The skill provides specific implementation patterns and best practices for Python, TypeScript, JavaScript, Rust, and Go.

How does this skill improve API reliability?

It guides you in creating standardized error responses and status codes, ensuring your APIs are predictable and easier for frontend teams to integrate with.

Does it include patterns for distributed systems?

Yes, it covers advanced patterns like the Circuit Breaker to prevent cascading failures and exponential backoff for handling transient network issues.

Robust Error Handling Patterns

Name: Robust Error Handling Patterns
Author: Microck

byMicrock

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API Development

Implements resilient error handling strategies, language-specific patterns, and fault-tolerant architectures to improve application reliability.

About

This skill equips developers with a comprehensive toolkit for managing application failures across diverse programming environments including Python, TypeScript, Rust, and Go. It provides structured guidance on choosing between exceptions and Result types, implementing custom error hierarchies, and applying advanced resiliency patterns like circuit breakers and exponential backoff. By standardizing how failures are caught, propagated, and reported, this skill helps ensure that applications fail gracefully while providing clear, actionable feedback for both end-users and debugging teams.

Key Features

Implementation of custom exception hierarchies and functional Result types
Best practices for developer-friendly error messages and logs
Language-specific patterns for Python, TypeScript, Rust, and Go
Guidance on error propagation and graceful degradation
Fault-tolerance strategies including Circuit Breakers and Retries
81 GitHub stars

Use Cases

Designing error-resilient REST and GraphQL APIs
Refactoring legacy codebases to implement modern error handling logic
Building fault-tolerant distributed systems that handle transient network failures

About

Key Features

Implementation of custom exception hierarchies and functional Result types
Best practices for developer-friendly error messages and logs
Language-specific patterns for Python, TypeScript, Rust, and Go
Guidance on error propagation and graceful degradation
Fault-tolerance strategies including Circuit Breakers and Retries
81 GitHub stars

Use Cases

Designing error-resilient REST and GraphQL APIs
Refactoring legacy codebases to implement modern error handling logic
Building fault-tolerant distributed systems that handle transient network failures