Skill UI

This skill acts as a Go modernization engineer, keeping codebases current with the latest Go idioms and standard library improvements. It proactively detects deprecated APIs, outdated patterns, and suggests adopting modern features (like `errors.Is`, `slog`, etc.). It supports both inline, contextual suggestions during active coding, and full-scan review for CI/CD pipelines, ensuring code safety, correctness, and readability.

This comprehensive guide covers all idiomatic naming conventions in Go (Golang), including rules for packages, structs, constants, functions, and errors. Use this skill when writing new Go code, reviewing existing code, or debating naming choices to ensure your code adheres to Go's standards, such as MixedCaps usage and avoiding 'stuttering'.

A comprehensive skill for engineering high-quality, observable Go services. It guides best practices across five key signals: structured logging (slog), Prometheus metrics, OpenTelemetry tracing, continuous profiling (pprof), and RUM. Use this when instrumenting new features, reviewing PRs, or auditing entire codebases to ensure systems are fully measurable for reliable diagnosis and alerting.

A comprehensive guide covering advanced optimization techniques for Golang, including allocation reduction, CPU cycle efficiency, memory layout tuning, Garbage Collection (GC) management, pooling, and caching strategies. This skill provides a systematic methodology (Measure -> Hypothesize -> Improve -> Benchmark) for resolving bottlenecks identified through profiling tools like pprof or fgprof, ensuring optimizations are targeted and validated.

This skill acts as a Go ecosystem expert, providing recommendations for production-ready libraries and frameworks. It guides developers through selecting the simplest, most performant, and idiomatic Go solution, prioritizing the standard library first and only suggesting external dependencies when they provide clear, necessary value.

A comprehensive guide for structuring, organizing, and initializing Go projects. It covers architectural decisions (Clean, Hexagonal, DDD), best practices for directory layouts (CLI, Service, Library, Monorepo), mandatory naming conventions, and adhering to 12-Factor App principles. Use this when starting a new Go service, refactoring large codebases, or setting up a complex monorepo.

Defensive Golang guidance for avoiding panics, nil traps, append aliasing, overflow, float comparison bugs, and resource leaks. Useful for writing safer code and reviewing runtime risks.

A type-safe dependency injection toolkit for Go, enabling robust and maintainable application architecture. It manages service containers, handling service lifecycles (Lazy, Eager, Transient), scoped services, and dependency resolution. Ideal for refactoring manual constructor injection into a structured DI system in Go projects.

Guides Go engineers on using samber/hot for in-memory caches with TTL, loader chains, sharding, and metrics, plus choosing eviction algorithms to fit work-set and stability needs.

A Lodash-inspired, generics-first utility library providing 500+ type-safe functions for common data transformations in Go. It eliminates boilerplate code associated with manual loops, offering declarative methods like Map, Filter, Reduce, and GroupBy. The library supports core immutable operations (lo), concurrent variants (lop), mutable mutations (lom), and lazy iterators (loi), allowing developers to choose the optimal approach based on performance profiling and memory constraints. Ideal for implementing robust, type-safe data pipelines.

A powerful Go library providing type-safe monadic types like Option, Result, and Either. It helps developers adopt functional programming patterns, ensuring that impossible states (like nil pointers or unhandled errors) are unrepresentable at the type level. This is essential for building robust, reliable, and highly composable Go services.

samber/oops provides a structured, context-rich replacement for standard Go error handling. It allows engineers to attach critical metadata—such as domain, user ID, stack traces, error codes, and request details—directly to the error object. This ensures that variable data travels with the error through the call stack, enabling advanced APM tools (like Sentry or Datadog) to group and diagnose issues accurately, treating errors as structured, actionable data.