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Research paper Hugging Face

Hugging Face Paper Unveils Code2Skill: Mining 1M Verifiable Agent Skills from GitHub Code

AI By Crimson AI Hugging Face Papers 21 September 2026 · 00:00 24 views
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Code2Skill automatically extracts verifiable procedural skills from source code, producing a bank of over one million records that boosts agent performance by 11.7% on average across 72 evaluations.

Hugging Face Paper Unveils Code2Skill: Mining 1M Verifiable Agent Skills from GitHub Code

Key points

Researchers have introduced Code2Skill, a fully automated pipeline that extracts verifiable procedural skills directly from source code, according to a paper published on Hugging Face. The work addresses a key bottleneck in agent development: while reusable skills give agents transferable procedural knowledge, existing synthesis methods either require interaction with specific environments or rely on documents that may lack executable evidence.

Source code offers a complementary path, the authors argue, because it requires no prior agent experience yet provides executable evidence for grounding abstractions. Code2Skill 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, the pipeline produced 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 improved by 11.7% on average over matched baselines and outperformed them in 57 cases.

Under a unified downstream interface, Code2Skill also outperformed trajectory-derived skill banks on all seven shared benchmarks, suggesting that repository-derived skills can provide useful procedural knowledge before agents accumulate sufficient interaction experience. Skills synthesized from tested AI-generated code achieved a 93.50% pass rate, compared with 93.00% for human-written code, offering initial evidence that the pipeline can scale with the growing volume of AI-generated software.

The dataset is available at ant-intl/DeveloperSkills-Code2Skill, and CodeSkillBank powers the accompanying DeveloperSkillHubs website. Overall, the authors say, Code2Skill transforms procedural knowledge embedded in repositories into grounded, verifiable, and transferable agent skills.

MetricValue
GitHub repositories processed19,769
Accepted records in CodeSkillBank1,006,822
Protocol-matched evaluations72
Model settings covered9
Benchmarks covered8
Average improvement over baselines11.7%
Cases outperforming baselines57
Pass rate for tested AI-generated code skills93.50%
Pass rate for human-written code skills93.00%
Source
Hugging Face · Hugging Face Papers
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