Grounded Skill Synthesis from Code at Scale for Agentic Intelligence
Code2Skill mines 19,769 GitHub repositories into CodeSkillBank, 1,006,822 verified skill records that lift agent benchmark performance by 11.7% on average.
Code2Skill transforms code units into implementation-anchored records of atomic operations, workflows, and patterns, verifying each via source-body-blind reconstruction and source-aware comparison. Applied to 19,769 popular repositories, it produces CodeSkillBank with 1,006,822 accepted records. Across 72 protocol-matched evaluations spanning nine model settings and eight benchmarks, retrieval-augmented models improve 11.7% on average and win 57 cases, beating trajectory-derived skill banks on all seven shared benchmarks; skills from AI-generated code pass at 93.50%.
- 1,006,822 verified skill records mined from 19,769 repositories
- Skills verified through source-body-blind reconstruction
- Agent benchmarks improve 11.7% on average across 72 evaluations
- AI-generated code skills pass verification at 93.50%
From the original article
1 min read at huggingface.co
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.
By Yongqi Tong, Pan Wang, Hang Wang · huggingface.co
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.