Organizing, Orchestrating, and Benchmarking Agent Skills at Ecosystem Scale
The rapid proliferation of Claude agent skills has raised the central question of how to effectively leverage, manage, and scale the agent skill ecosystem. In this paper, we propose AgentSkillOS, the first principled framework for skill selection, orchestration, and ecosystem-level management. AgentSkillOS comprises two stages: (i) Manage Skills, which organizes skills into a capability tree via node-level recursive categorization for efficient discovery; and (ii) Solve Tasks, which retrieves, orchestrates, and executes multiple skills through DAG-based pipelines. To evaluate the agent's ability to invoke skills, we construct a benchmark of 30 artifact-rich tasks across five categories: d...
| Authors | Hao Li, Chunjiang Mu, Jianhao Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
Connected in the knowledge graph
- NPULH/agentskillos-benchmarkDataset · cited by
- zjunlp/SkillNet-FabricDataset · cited by
Models and datasets referencing this paper
- NPULH/agentskillos-benchmarkDataset
- zjunlp/SkillNet-FabricDataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.