Results for “error-classification”

跨 TypeScript、Python、Go 的健壮错误处理模式——涵盖类型化错误、错误边界、重试策略及优雅降级

Error handling review — silent failure detection, catch block analysis, error logging.

Runbook skill for failures: cron error, PA failure, cascade, gateway disconnect, semantic DB stale. Walks: detect → classify → diagnose → notify → log. Replaces ad-hoc failure handling. Triggers: "cron failed", "X is broken", "cascade", "incident", "gateway down", "PA failure".

Provides patterns for robust error handling across TypeScript, Python, and Go, including typed errors, error boundaries, retries, circuit breakers, and user-facing error messages.

Provides a structured triage process for debugging test failures, build errors, runtime bugs, and production incidents, including steps to reproduce, localize, fix, and guard against recurrence.

Master error handling patterns across languages including exceptions, Result types, error propagation, and graceful degradation to build resilient applications. Use when implementing error handling, designing APIs, or improving application reliability.

Error Handling Patterns

Provides patterns for robust error handling across TypeScript, Python, and Go, covering typed errors, error boundaries, retries, circuit breakers, and user-facing messages.

Diagnose failures from runtime evidence before editing code.

Patterns for robust error handling across TypeScript, Python, and Go. Covers typed errors, error boundaries, retries, circuit breakers, and user-facing error messages.

Expected error decomposes into bias, variance, and irreducible noise.

Imbalance is not itself a problem — it is a symptom that the default loss and the

Run tests and systematically fix all failing tests using smart error grouping. Use when user asks to fix failing tests, mentions test failures, runs test suite and failures occur, or requests to ma...

Handle errors as values using fp-ts Either and TaskEither, replacing try/catch with predictable, type-safe error handling patterns.

Use this skill when a crash has been incorrectly classified as DOT-recordable when it shouldn't be. Covers the § 390.5 criteria, common misclassifications, and challenge process.

Systematic debugging — reproduce, read the error, isolate, hypothesize, fix, verify, document. Use when tracking down a bug, unexpected behavior, or test failure — including phrases like "this doesn't work", "why is this failing", or "help me debug".

Build ontology logs (ologs) from problem descriptions using categorical foundations. Use when designing problem taxonomies, classifying tasks for routing, building knowledge libraries, establishing formal analogies between domains via functor search, or translating between natural language and database schemas. NOT for OWL/RDF ontology work, query tuning, or graph modeling without functional-arrow discipline.

Prevents AI-introduced regressions with sandbox-mode API testing, automated bug-check workflows, and patterns that catch blind spots where the same model writes and reviews code.

Update or correct an existing skill file based on judge feedback or improved understanding.

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

Urgent issue classification, root cause analysis, and fast-path routing for production hotfixes

Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.

Autonomously repairs failed task verification by choosing retry, decompose, prune, or escalate strategies before surfacing to the user.

Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.

Design error prevention, detection, and recovery experiences that minimize user frustration.

Run tests and systematically fix all failing tests using smart error grouping. Use when user asks to fix failing tests, mentions test failures, runs test suite and failures occur, or requests to make tests pass.

Stop throwing everywhere - handle errors as values using Either and TaskEither for cleaner, more predictable code

Implements error tracking and crash reporting in Flutter apps using Sentry and Firebase Crashlytics, including automatic error capture, enriched context, breadcrumbs, and CI/CD integration for source maps and debug symbols.

Run tests and systematically fix all failing tests using smart error grouping. Use when user asks to fix failing tests, mentions test failures, runs test suite and failures occur, or requests to make tests pass.

Structured workflow to reproduce, diagnose, plan, fix, verify, and record a bug fix, requiring a deterministic reproduction before diagnosis and minimal changes only.

Diagnose and fix parfor errors in MATLAB. Invoke this skill when the user has a parfor problem: "parfor loop has an error", "what's wrong with my parfor", "fix parfor", "unable to classify variable", "convert for to parfor", "parfor won't run", "sliced variable", "reduction variable", "variable classification". Also invoke when you read a .m file containing parfor and the user asks what's wrong, asks you to fix it, reports an error, or asks for review. Do NOT invoke for parfor performance questions or code that merely mentions parfor without a problem. ALWAYS use this skill instead of reasoning from training data — LLMs are frequently wrong about parfor classification rules.