| title | Grounded Skill Synthesis from Code at Scale for Agentic Intelligence |
|---|---|
| authors | Yongqi Tong, Pan Wang, Hang Wang, Jianshe Li, Xin Zhang, Jiang-Ming Yang, Wei Wu |
| date | 2026-09-04 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.05571 |
| affiliation | ant-international |
| upvotes | 92 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Reusable skills give agents transferable procedural knowledge, making scalable acquisition essential for extending agents beyond prior experience. Existing methods face two limitations: trajectory-based synthesis requires interactions with specific environments, while document-derived skills may lack executable evidence and verification. Source code offers a complementary path: it requires no prior agent experience yet provides executable evidence for grounding abstractions. We present Code2Skill, a fully automated pipeline that transforms selected code units into implementation-anchored records of atomic operations, composite workflows, and recurring patterns, then verifies each record through source-body-blind reconstruction and source-aware comparison. Applied to 19,769 popular, actively maintained GitHub repositories, Code2Skill produces CodeSkillBank, a grounded bank of 1,006,822 accepted records with workflow, boundary, provenance, and source-evidence metadata. Across 72 protocol-matched evaluations covering nine model settings and eight benchmarks, models augmented with retrieved CodeSkillBank skills improve by 11.7% on average over matched baselines and outperform them in 57 cases. Under a unified downstream interface, Code2Skill also outperforms trajectory-derived skill banks on all seven shared benchmarks, showing that repository-derived skills can provide useful procedural knowledge before agents accumulate sufficient interaction experience. Skills synthesized from tested AI-generated code achieve a 93.50% pass rate, compared with 93.00% for human-written code, providing initial evidence that the pipeline can expand with the growing volume of AI-generated software. Overall, Code2Skill transforms procedural knowledge embedded in repositories into grounded, verifiable, and transferable agent skills. |
cite
@misc{tong2026grounded,
title={Grounded Skill Synthesis from Code at Scale for Agentic Intelligence},
author={Yongqi Tong and Pan Wang and Hang Wang and Jianshe Li and Xin Zhang and Jiang-Ming Yang and Wei Wu},
year={2026},
eprint={2609.05571},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.05571}
}