@inproceedings{liu-etal-2026-skillsvote,
title = "{S}kills{V}ote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution",
author = "Liu, Hongyi and
Yang, Haoyan and
Jiang, Tao and
Tang, Bo and
Xiong, Feiyu and
Luo, Yuyu and
li, Zhiyu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.281/",
pages = "6401--6462",
ISBN = "979-8-89176-459-0",
abstract = "Long-horizon LLM agents generate traces that could become reusable experience, but raw trajectories are noisy, local, and hard to govern. Agent Skills offer a structured artifact for combining procedural guidance, executable resources, and applicability boundaries. Yet open skill ecosystems contain redundant, uneven, environment-sensitive artifacts, and indiscriminate updates can pollute future context. We present SkillsVote, a lifecycle-governance framework for Agent Skills across collection, recommendation, attribution, and evolution. SkillsVote profiles a million-scale open-source corpus for environment requirements, quality, and verifiability, and synthesizes tasks for verifiable skills. Before execution, it performs agentic library search over structured skill folders to expose instructional context. After execution, it decomposes trajectories into skill-linked subtasks, attributes outcomes to skill-guided execution, agent exploration, environment, and result signals, and admits only successful reusable discoveries to evidence-gated updates. Experiments on Terminal-Bench 2.0 and SWE-Bench Pro show that SkillsVote improves agent performance on challenging agentic coding benchmarks. The gains arise from two complementary pathways: online evolution over test-time task streams and offline transfer via frozen libraries built from either historical trajectories or curated open-source skills. We release our code at https://github.com/MemTensor/skills-vote."
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<abstract>Long-horizon LLM agents generate traces that could become reusable experience, but raw trajectories are noisy, local, and hard to govern. Agent Skills offer a structured artifact for combining procedural guidance, executable resources, and applicability boundaries. Yet open skill ecosystems contain redundant, uneven, environment-sensitive artifacts, and indiscriminate updates can pollute future context. We present SkillsVote, a lifecycle-governance framework for Agent Skills across collection, recommendation, attribution, and evolution. SkillsVote profiles a million-scale open-source corpus for environment requirements, quality, and verifiability, and synthesizes tasks for verifiable skills. Before execution, it performs agentic library search over structured skill folders to expose instructional context. After execution, it decomposes trajectories into skill-linked subtasks, attributes outcomes to skill-guided execution, agent exploration, environment, and result signals, and admits only successful reusable discoveries to evidence-gated updates. Experiments on Terminal-Bench 2.0 and SWE-Bench Pro show that SkillsVote improves agent performance on challenging agentic coding benchmarks. The gains arise from two complementary pathways: online evolution over test-time task streams and offline transfer via frozen libraries built from either historical trajectories or curated open-source skills. We release our code at https://github.com/MemTensor/skills-vote.</abstract>
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%0 Conference Proceedings
%T SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution
%A Liu, Hongyi
%A Yang, Haoyan
%A Jiang, Tao
%A Tang, Bo
%A Xiong, Feiyu
%A Luo, Yuyu
%A li, Zhiyu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-skillsvote
%X Long-horizon LLM agents generate traces that could become reusable experience, but raw trajectories are noisy, local, and hard to govern. Agent Skills offer a structured artifact for combining procedural guidance, executable resources, and applicability boundaries. Yet open skill ecosystems contain redundant, uneven, environment-sensitive artifacts, and indiscriminate updates can pollute future context. We present SkillsVote, a lifecycle-governance framework for Agent Skills across collection, recommendation, attribution, and evolution. SkillsVote profiles a million-scale open-source corpus for environment requirements, quality, and verifiability, and synthesizes tasks for verifiable skills. Before execution, it performs agentic library search over structured skill folders to expose instructional context. After execution, it decomposes trajectories into skill-linked subtasks, attributes outcomes to skill-guided execution, agent exploration, environment, and result signals, and admits only successful reusable discoveries to evidence-gated updates. Experiments on Terminal-Bench 2.0 and SWE-Bench Pro show that SkillsVote improves agent performance on challenging agentic coding benchmarks. The gains arise from two complementary pathways: online evolution over test-time task streams and offline transfer via frozen libraries built from either historical trajectories or curated open-source skills. We release our code at https://github.com/MemTensor/skills-vote.
%U https://aclanthology.org/2026.emnlp-main.281/
%P 6401-6462
Markdown (Informal)
[SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution](https://aclanthology.org/2026.emnlp-main.281/) (Liu et al., EMNLP 2026)
ACL
- Hongyi Liu, Haoyan Yang, Tao Jiang, Bo Tang, Feiyu Xiong, Yuyu Luo, and Zhiyu li. 2026. SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 6401–6462, Budapest, Hungary. Association for Computational Linguistics.