LLMs increasingly use external tools and stateful environments for complex, multi-step tasks. Further improving their tool-use capabilities, however, remains bottlenecked by the lack of scalable stateful environments and verified multi-turn trajectories matched to the target LLM’s current capability boundary. We observe that agent skills, packages pairing workflow specifications with executable scripts, provide scalable seed material for tool-use environment synthesis. This motivates Grafting, a skill-to-environment synthesis loop in which coding-agent harnesses perform open-ended construction under deterministic validation contracts, producing self-contained executable environments that are refined using trajectory-level verdicts. In an initial end-to-end run, Grafting produces 500 environments and approximately 12K verified multi-turn trajectories. We train on these trajectories with Fathoming, an iterative GRPO procedure that selects verified trajectories near the target model’s capability boundary. Training a 4B-parameter instruction-tuned model with two rounds of Fathoming on the trajectory pool produces Fathom-4B and yields strong out-of-distribution gains: BFCLv4 multi-turn accuracy rises from 22.0 to 39.7 (+17.7 points), and ComplexFuncBench per-call accuracy rises from 49.6 to 61.6 (+12.0 points). Using the same environment-synthesis pipeline, we further construct Grove-5K, a collection of 5,101 executable environments. We release Grafting, Grove-500, Grove-5K, and Fathom-12K.