error-handling-patterns

Error handling strategies for distributed systems use propagation rules, retries, circuit breakers, and graceful degradation to keep systems stable. This Skill designs robust patterns for async workflows, APIs, and background jobs to ensure predictable failures and clean recovery.

Use Result types instead of exceptions when you need explicit, typed error modeling without control flow interruption. This Skill helps choose between exceptions, Result types, and custom error classes based on the specific failure type and production-grade validation requirements.

Preserve context during a network call failure by applying context-preserving logging and typed error modeling. This Skill reviews code paths to suggest concrete improvements for propagation, cleanup, and surfacing meaningful messages when payments or network requests fail.

Yes, cross-language guidance applies practical error handling patterns for Python, TypeScript, Rust, and Go. This Skill provides specific recovery patterns, custom error classes, and propagation rules tailored to building APIs and distributed services in these languages.

The best way to implement retries and circuit breakers for background jobs is applying recovery patterns that decide whether to retry, fall back, or re-raise. This Skill designs cleanup flows and stability mechanisms for systems under failure.

Applications crash unpredictably when lacking graceful degradation and typed error propagation. This Skill designs robust error handling strategies with cleanup flows and recovery patterns so applications fail predictably, recover cleanly, and remain easier to debug.