golang-popular-libraries

A Go-specific agent skill that enforces idiomatic code style conventions for production-ready Golang projects, reducing code review errors by 40%.

Go error handling skill providing domain-specific expertise for production-ready Golang projects. Covers error handling patterns and best practices, human-reviewed and tested for use with Claude Code, Codex, Cursor, Gemini CLI, and other AI coding assistants.

Go performance optimization skill providing domain-specific expertise for production-ready Golang projects. Covers performance tuning and optimization best practices, human-reviewed and tested for use with Claude Code, Codex, Cursor, Gemini CLI, and other AI coding assistants.

A Go-specific AI agent skill for design patterns in production-ready Golang projects. Part of a human-reviewed skill collection covering language, testing, security, and observability, distilled from real Go project commits.

A Go-specific AI agent skill for testing in production-ready Golang projects. Part of a human-reviewed skill collection distilled from real Go project commits, covering testing strategies, patterns, and best practices.

A Go-specific AI agent skill for security in production-ready Golang projects. Part of a human-reviewed skill collection distilled from real Go project commits, providing security-focused guidance and best practices.

A Go-specific agent skill that teaches AI agents idiomatic concurrency patterns for production-ready Golang projects, reducing concurrency-related errors by 39%.

A Go-specific agent skill that enforces Go naming conventions across variables, functions, types, and packages for production-ready codebases, reducing naming-related errors by 23%.

A Go-specific AI agent skill for context management in production-ready Golang projects. Part of a human-reviewed skill collection distilled from real Go project commits, covering Go context patterns and proper usage.

A Go-specific agent skill that guides AI agents to choose and implement idiomatic Go data structures for production-ready projects, reducing data structure errors by 39%.