SkillGym turns 184k community skills into verifiable training environments, and its 9B fine-tuned model beats a 397B untrained model on two benchmarks
SkillGym proposes an automatic pipeline that crawls skills from the internet and keeps those whose workflows run reproducibly offline, uses a builder-reviewer agent system to construct skill-critical tasks across four reasoning structures each with a reference solution and an executable verifier, then collects successful trajectories from three teacher models and four agent harnesses for supervised finetuning, improving six models from 2B to 122B parameters on 22 of 24 comparisons across four skill-use benchmarks, with the 9B model outperforming Qwen3.5-397B-A17B on the SkillGym test set and SkillEval and raising the rate of reading the relevant skill from 28% to 96%.