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 knowledge can be reused across tasks rather than rediscovered during each run. We present DisCo, a skill-powered research agent that creates skills and uses them during research. Its distillation runs in two complementary forms: task-agnostic, condensing the field’s widely used repositories into reusable skills, and task-oriented, producing the skills a concrete task calls for. The former, applied across the open ecosystem, yields the AREX-Skill Library, with 5,000+ verified skills distilled from 1,000 widely used ML repositories and organized into 20 areas and 178 capability families.

Introduction. Autonomous agents are beginning to execute larger parts of the machine-learning (ML) research pipeline, from implementing methods to running experiments and comparing results (Lu et al., 2024; Yamada et al., 2025; Schmidgall et al., 2025). ML research is a natural testbed because much of its practice unfolds in software, where coding agents have proved most capable (Jin et al., 2026; Dong et al., 2026). Like any agentic system, these research agents rest on two modules: a model that supplies understanding, reasoning, planning, and execution, and a harness that supplies orchestration, memory, verification, and iterative refinement. The model improves with frontier generations, and the harness improves through engineering practice (Karpathy, 2026). ML research, however, is expertise-intensive, which means success depends on knowing which methods and tools to use, when to use them, and how to use them correctly. Neither component carries this expertise. The model’s prior is broad but fixed, while the harness controls procedure but does not supply domain content.

Discussion / Conclusion. In this paper, we study operational knowledge as a missing layer for ML research agents. DisCo fills this layer by distilling source knowledge into reusable operational-knowledge skill graphs that can be loaded as operating context while leaving the model backbone and research harness unchanged. Scaling DisCo yields the AREX-Skill Library, whose repository snapshot contains 5,000+ skills distilled from 1,000 widely used ML repositories. We also construct paper-derived and task-oriented skills for the research settings evaluated in this work. Under a fixed GPT-5.5 Codex setup and matched downstream budgets, the skill-equipped agent scores 134.3% higher on MLE-bench, 34.4% higher on PaperBench, 9.2% higher on FrontierCS, and 14.0% higher on PassNet. These results support the central claim that autonomous research agents can improve by adding operational knowledge rather than relying only on stronger control loops.