Generate idiomatic Go code for Gin-based HTTP servers and Lua plugins.
npx skills add https://github.com/traylinx/switchAILocal --skill go-expert-traylinx
Please help me install this Agent Skill. Skill: go-expert Source: https://github.com/traylinx/switchAILocal/tree/main/plugins/cortex-router/skills/go-expert Command: npx skills add https://github.com/traylinx/switchAILocal --skill go-expert-traylinx
Go-expert provides expert guidance to write professional, idiomatic Go code tailored for the switchAILocal codebase, including Gin-based HTTP servers and Lua plugin integrations.
To begin, request Go idiomatic code scaffolding for a Gin-based proxy with Lua scripting support in switchAILocal.
High-intent search queries and answers about installing and using this skill.
Idiomatic Go emphasizes structured error wrapping, safe concurrency with mutexes and wait groups, consistent logging, and clear module organization. This approach ensures professional, maintainable code that scales reliably in production environments.
Yes. Gin-based HTTP servers are a primary use case for professional Go development. Go-expert provides guidance on project structure, concurrency patterns, and context cancellation correctness needed for high-performance proxy implementations.
Lua-scripted plugins can extend Go services within the switchAILocal framework. Go-expert covers plugin architecture, safe concurrency when executing Lua code, and consistent error handling across plugin boundaries.
Go concurrency relies on mutexes for shared state, wait groups for synchronization, and proper context cancellation to prevent goroutine leaks. Idiomatic patterns ensure safe, predictable behavior under load.
Structured error wrapping in Go enables clear error propagation and debugging. Idiomatic error handling combines explicit error checking with context-aware wrapping to maintain transparency across service layers.
Professional Go projects use internal/, plugins/, and sdk/ directory layouts to enforce encapsulation and clarity. Clear module organization reduces coupling, improves testability, and scales with team size.
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