The researchers introduced EmbodiedSkills, a unified framework designed to transform Vision-Language-Action (VLA) models into robust embodied agents capable of managing long-horizon tasks. Unlike traditional models that rely solely on direct action prediction, EmbodiedSkills treats every skill decision as an "execution proposal." This approach utilizes a fixed, executable-skill interface that orchestrates a closed-loop cycle consisting of high-level skill selection, bounded low-level execution, prerequisite checking, and post-action outcome verification. By decoupling the agent loop from the specific VLA policy, the framework allows for modular replacement of low-level controllers while recording structured trajectories of planning, execution, and recovery for future supervision or online adaptation. The authors instantiated the framework using Qwen3-VL and OpenPI/pi0.5 to evaluate performance across several benchmarks. On the RoboTwin 2.0 dataset, task-adapted low-level policies achieved an average success rate of 86.20% across 50 tasks, while performance reached 97.40% across the four LIBERO suites. However, the framework faced significant challenges in complex reasoning environments, achieving only a 12.5% average success rate on four memory-dependent RMBench tasks. Ultimately, EmbodiedSkills provides a trainable and inspectable architecture for converting reactive policies into sophisticated, self-verifying robotic systems.