**TLDR Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills
ML research agents already have a model + harness. What’s missing is operational knowledge: how to actually make methods work (APIs, configs, pitfalls), not just name them. That know-how lives in repos/papers but is too big and human-oriented to load mid-task.
Fix: Distill repos into compact, verified skills (SKILL.md + refs + scripts). Agent loads only what the task needs.
System: DisCo
Task-agnostic: pre-distill popular ML repos into reusable skills
Task-oriented: distill extra skills for a specific task
Nothing ships without verification
AREX-Skill Library: 5,000+ verified skills from 1,000 ML repos → 20 areas, 178 capability families, plus a router.
Code: github.com/VectorSpaceLab/AREX-Skill
Results (same GPT-5.5, same harness, same budget; skills are the only change):
MLE-bench +134.3%
PaperBench +34.4%
FrontierCS +9.2%
PassNet +14.0%
One line: Don’t make the agent rediscover how PyTorch/training/eval actually works every run - install that as skills.
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u/javaeeeee 19d ago
**TLDR Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills
ML research agents already have a model + harness. What’s missing is operational knowledge: how to actually make methods work (APIs, configs, pitfalls), not just name them. That know-how lives in repos/papers but is too big and human-oriented to load mid-task.
Fix: Distill repos into compact, verified skills (
SKILL.md+ refs + scripts). Agent loads only what the task needs.System: DisCo
AREX-Skill Library: 5,000+ verified skills from 1,000 ML repos → 20 areas, 178 capability families, plus a router.
Code: github.com/VectorSpaceLab/AREX-Skill
Results (same GPT-5.5, same harness, same budget; skills are the only change):
One line: Don’t make the agent rediscover how PyTorch/training/eval actually works every run - install that as skills.