[robbomacrae]

SCB is definitely an underrated benchmark. For me the unique selling point is that it more closely mirrors software development by not stopping after a single task. The agent has to keep code clean. The only disadvantage is all the problems are greenfield and not git inited so the agents don’t make use of git diffs.

I’ve used SCB as part of my assessment of agent skills (superpowers, GSD etc) https://orcabot.com/labs/do-skills-improve-coding-agent-accu...

There is a small but growing community on discord for discussing SCB so if interested please join https://discord.gg/BrC4BA9sVj

[gck1]

I did a full circle and essentially dropped all of my personal static workflows encoded in skills because I observed recent models picking better ad-hoc workflows for particular problems, when a static one would force a subpar one.

It seems like we all tried to contain and organize a system that simply prefers to select its own organization.

Which makes me to think that these skill packs of workflows are really made to make it easier for humans rather than agents.

[robbomacrae]

Thats right and to go even further I'm judging them on a metric they didn't necessarily target. A client I work with uses skills such as these to apply their own processes on the agentic development lifecycle. But users should also understand the trade-offs. I think it's intuitive that the extra steps and processing invoked by these skills adds to the token cost - this benchmark aims to put numbers on that as well as time and accuracy.

[dhorthy]

so wait is the finding that most of those skills reduce pass rates against SCB? wild

[robbomacrae]

Yep that was interesting but imho not completely unexpected. My original hypothesis was that these agent skills are castles built on sand and that the models would quickly adapt and make them redundant. Also the skills use up context and I think a lot of them partially focus on investigating a large complex codebase properly... which is wasted on greenfield projects.