Very interesting, especially the harness point, how much of performance is in the wrapper tools (when I almost run out of credits, I change my model to a smaller one and try to give it more structured prompts; very often gpt-5.4-mini with structure works better than gpt-5.4 with vibes)

This inspired me to start a "skill distillery" [0] where I take good agent workflow ideas and turning them into small, inspectable/installable skills.

The first one is dirac-workflow, based on Dirac's structural code workflow. It's not a Dirac clone tho, it has no runtime, persistent AST index, hash-anchor editing engine, or benchmark harness. Just a small AST helper and the workflow discipline as a portable skill.

I also dogfooded it on the Dirac repo itself and included a short report.

Would appreciate feedback from the original author, if the prompts and tools [1] are representative.

[0] https://github.com/ouatu-ro/skill-distillery

[1] https://github.com/ouatu-ro/skill-distillery/blob/main/skill...