Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.02709 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915978215948288 |
|---|---|
| author | Xu, Gelei Tang, Ningzhi Li, Xueyang Li, Toby Jia-Jun Zheng, Zhi Jin, Wei Shi, Yiyu |
| author_facet | Xu, Gelei Tang, Ningzhi Li, Xueyang Li, Toby Jia-Jun Zheng, Zhi Jin, Wei Shi, Yiyu |
| contents | Healthcare automation is shaped by local procedures and organizational constraints, so agent capabilities rarely transfer unchanged across settings. Agent skills, self-contained directories that package reusable procedures for AI agents, are emerging as a procedural layer for adapting healthcare agents across diverse healthcare settings. We present the first empirical analysis of healthcare agent skills, drawing on 557 healthcare-related skills filtered from 58,159 public skills on ClawHub and annotated along ten dimensions covering function, deployment context, autonomy, and safety. We find that public healthcare skills emphasize patient-facing workflow automation and monitoring rather than the diagnostic and treatment-oriented tasks foregrounded in healthcare-agent research; coverage of the healthcare lifecycle and specialized clinical inputs remains uneven; and general technical risk does not reliably capture clinical risk. These findings position healthcare skills as a procedural layer not yet addressed by current benchmarks and risk frameworks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02709 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Empirical Study of Agent Skills for Healthcare: Practice, Gaps, and Governance Xu, Gelei Tang, Ningzhi Li, Xueyang Li, Toby Jia-Jun Zheng, Zhi Jin, Wei Shi, Yiyu Artificial Intelligence Healthcare automation is shaped by local procedures and organizational constraints, so agent capabilities rarely transfer unchanged across settings. Agent skills, self-contained directories that package reusable procedures for AI agents, are emerging as a procedural layer for adapting healthcare agents across diverse healthcare settings. We present the first empirical analysis of healthcare agent skills, drawing on 557 healthcare-related skills filtered from 58,159 public skills on ClawHub and annotated along ten dimensions covering function, deployment context, autonomy, and safety. We find that public healthcare skills emphasize patient-facing workflow automation and monitoring rather than the diagnostic and treatment-oriented tasks foregrounded in healthcare-agent research; coverage of the healthcare lifecycle and specialized clinical inputs remains uneven; and general technical risk does not reliably capture clinical risk. These findings position healthcare skills as a procedural layer not yet addressed by current benchmarks and risk frameworks. |
| title | An Empirical Study of Agent Skills for Healthcare: Practice, Gaps, and Governance |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.02709 |