SkillReducer cuts agent skill-file tokens by 77.5%

SkillReducer strips more than half the prose out of a typical agent skill file and still keeps full capability, according to new research posted to arXiv.

More than 60% of what's written in a typical SKILL.md file never changes what the agent actually does when it runs, the paper found. Some reference files tacked onto skills quietly inject tens of thousands of extra tokens into every single call.

The fix isn't a careful rewrite. Mechanical compression, cutting narrative and pushing detail into on-demand files, trimmed tokens by a mean of 77.5% across the tested skills. Capability didn't drop.

That's a cost most people writing agent skills haven't priced in. Every SKILL.md file gets read on every relevant call, so extra prose is a recurring token cost, not a one-time file-size problem. If I'm writing instructions an agent will use repeatedly, the backstory and caveats belong in a file it loads on demand. The main file should hold only what changes behavior.

The paper doesn't say how many distinct skills it tested this compression against, or whether the 77.5% figure holds for skills with heavy branching logic. But the direction is clear: write the skill file for the model that executes it, not for the person reading it once.