A developer has released QUALITY.md, an open format and command-line interface designed for holistic quality evaluation and loop engineering.
The tool arrives as AI assistance becomes more integrated into software development. By providing a standardized specification for quality, the project seeks to stabilize how developers measure and maintain the performance of AI-driven systems.
QUALITY.md functions as both a specification and an agent skill, allowing AI tools to better understand and execute quality benchmarks. The creator designed the system to address the gaps in traditional quality management, which often relies on fixing errors after they occur rather than preventing them through structured design.
"I created QUALITY.md to help build a holistic quality evaluation process for my projects," the author said in a post on Hacker News.
The author said that the tool is also intended for loop engineering, which involves the iterative process of refining AI prompts and outputs to achieve a specific standard of excellence. This approach aims to reduce the friction between initial deployment and final quality assurance.
Beyond the technical utility of the CLI, the project is framed as a philosophical shift in how software is built. The author said, "I hope to shift the mindset from a reactive/review/repair mindset to a proactive care mindset."
QUALITY.md is currently available as an open-source project via GitHub and a dedicated website. The framework allows developers to define quality expectations in a machine-readable format, enabling AI agents to self-correct, or flag deviations from the specified quality standards, before the code reaches a human reviewer.
“I created QUALITY.md to help build a holistic quality evaluation process for my projects.”
The introduction of QUALITY.md reflects a broader trend in AI development toward 'evals'—the creation of rigorous, automated testing frameworks to replace manual oversight. As AI agents take on more autonomous coding tasks, the industry requires a standardized way to define 'quality' that both humans and machines can interpret, reducing the risk of regression in complex software loops.
