error-handling-review

What problem does it solve?

This Skill helps reviewers ensure error handling is correct, consistent, and resilient, so failures don’t become silent corruption, confusing outages, or user-facing dead ends.

Core Features & Use Cases

• Error taxonomy enforcement: Categorizes findings by domain, validation, infrastructure, programming, and cancellation semantics so recommendations match the failure meaning.
• Detection of common anti-patterns: Flags swallowed errors, overly broad exception handling, lost context, weak wrapping, unchecked cleanup returns, and language-specific mistakes.
• Resilience and failure-mode coverage: Reviews missing timeouts, retry/circuit-breaker quality, partial failure semantics, DLQ strategy for poison messages, and user-facing message quality.
• Severity grading with required taxonomy: Produces actionable output grouped by Blocking, Recommendation, and Observation, ensuring review items cite the violated category.

Quick Start

Ask your AI reviewer to run an error-handling review using the error-handling-review reference and produce findings grouped by severity with each finding tied to an explicit taxonomy category.