🧩 What autonomous AI agents are missing isn't a smarter model — it's on-the-ground know-how. That's the premise of this paper. Title: Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills URL: ❓ What's missing from autonomous research agents? 💡 The usual two-layer view — model plus execution harness — leaves out the operational knowledge of picking the right method, using package APIs correctly, and avoiding implementation pitfalls. This paper treats that as an explicit third layer. ❓ How do you actually get that knowledge? 💡 It distills GitHub repos and papers through a four-stage pipeline — Scope, Ground, Construct, Verify — into verified "skills." From 1,000 repos and 153 papers, they built a library of 5,353 skills. ❓ How much difference do skills actually make? 💡 With the same GPT-5.5 backbone and same harness, just adding skills lifts MLE-bench from 31.11% to 72.89%, with similar gains across PaperBench, FrontierCS, and PassNet — hard tasks see over 4x improvement. ❓ Isn't this just throwing more compute at the problem? 💡 No — improvement barely correlates with token counts or tool calls, and it beats a Claude Opus 4.8 setup while using fewer tokens. The knowledge itself is doing the work. #AIAgents# #MachineLearning#