Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills
Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for planning, execution, memory, and verification, but this architecture still leaves domain-specific know-how outside the agent. We call this missing layer operational knowledge, the know-how that separates knowing a method from making it work. That knowledge is not absent from the field. It appears in repositories and papers, but in forms written for human readers and too large to load during a task. Once distilled into compact, verified skills, this kn
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzySimilar title/name (fuzzy) · 59%virgiliojr94/book-to-skill →
“Fuzzy title match (0.73): “Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Ski” ≈ “virgiliojr94/book-to-skill””
- FuzzySimilar title/name (fuzzy) · 59%KKKKhazix/khazix-skills →
“Fuzzy title match (0.73): “Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Ski” ≈ “KKKKhazix/khazix-skills””
- LinkedLinked via arxiv author · 85%Jianlyu Chen →
“Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills”
- LinkedLinked via arxiv author · 85%Yuyang Hu →
“Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills”
- LinkedLinked via arxiv author · 85%Hongjin Qian →
“Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills”
- LinkedLinked via arxiv author · 85%Jiawei Liu →
“Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills”
- LinkedLinked via arxiv author · 85%Wenqing Wei →
“Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills”