skill-library

Introduces SkillCenter, the largest open skill library for AI agents with 216,938 structured, source-grounded skills across 24 domains, built using an automated pipeline with an LLM-based quality gate called SkillGate. The library combines peer-reviewed and community skills, emphasizing traceability and offline searchability.

Fable 5 is transitioning to a pay-per-use model; users are advised to have it train its replacement and write skill files for continued use. The thread details a prompt to have Fable audit a repo and create a comprehensive skill library.

NVIDIA's ASPIRE framework enables robots to build a persistent library of skills from successful experiences, allowing reuse for new tasks and improving learning efficiency over time.

Introducing ASPIRE, a framework for robots to continuously evolve a library of skills through evolutionary search and distillation, enabling efficient sim-to-real and cross-embodiment transfer with up to 10x reduction in transfer learning tokens. The full stack is open-sourced.

VisualSkill proposes a hierarchical multimodal skill library for computer-use agents that combines text and figures, achieving a 15.3 point absolute lift on CUA benchmarks over text-only baselines by retaining visual information for GUI interaction.

Introduces Playful Agentic Robot Learning, where embodied coding agents use self-directed play to learn reusable skills, improving downstream task performance without additional training. The proposed RATs system achieves significant gains over baselines in simulation and real-world transfer.

This paper proposes Embodied-BenchClaw, an autonomous multi-agent system that automatically constructs embodied spatial intelligence benchmarks from user intent through a five-stage pipeline with process quality control and an extensible Skill Library.

This article argues that the first AI strategy for companies should be creating a 'skill library' to capture the reusable working methods of top performers, so agents can learn the method behind tasks rather than just access data. It promotes a live webinar called Skills 101.

This MUSE-Autoskill paper focuses on how an Agent manages an entire skill library, placing skills into a complete lifecycle: creation, memory, management, evaluation, and re-optimization.

SkillGraph is a framework that represents reusable skills as nodes in a directed graph to enable large language model agents to handle compositional tasks more effectively through structured skill retrieval and continuous evolution.

Skill1 is a unified framework that trains a single policy to co-evolve skill selection, utilization, and distillation using a shared task-outcome objective. Experiments on ALFWorld and WebShop show it outperforms existing baselines in complex task environments.