Show HN: Kiss – code-complexity feedback for LLM coding agents (github.com)

A new tool called "Kiss" has been launched to provide code quality feedback specifically designed for Large Language Model (LLM) coding agents. Kiss helps assess code complexity, duplication, and coverage by integrating feedback mechanisms such as the `kiss check` command, which ensures that various metrics and rules are met before code can be considered valid. Users can configure Kiss to align with their existing codebases, employing features like `kiss clamp` to gradually adopt stricter metrics over time and promote clearer, more maintainable code. This development is significant for the AI and machine learning community as it enhances the ability of LLMs to produce high-quality, reliable code, reducing complexity and the occurrence of code smells. Kiss establishes a framework of "mandatory rules" to guide LLMs during coding tasks, which aids in preventing common pitfalls like overly complex functions and dependencies. The ability to analyze and mimic existing code structures allows for a smoother integration into varying coding environments, making it a valuable asset for developers looking to optimize their use of AI tools in software development.