<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lu-etal-2026-skillme">
    <titleInfo>
        <title>SkillMe: Evolving Agent Skills for Recurring Personal Workflows</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xiuqi</namePart>
        <namePart type="family">Lu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jie</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yuanfu</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lewen</namePart>
        <namePart type="family">Yan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yutong</namePart>
        <namePart type="family">Zhi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jing</namePart>
        <namePart type="family">Shao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Hung-Yi</namePart>
            <namePart type="family">Lee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sunipa</namePart>
            <namePart type="family">Dev</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jessy</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Desmond</namePart>
            <namePart type="family">Elliott</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Budapest, Hungary</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-459-0</identifier>
    </relatedItem>
    <abstract>Skills help LLM agents reuse procedural knowledge in recurring workflows, but broad transferability alone does not guarantee workflow fit: a reusable skill may be semantically relevant yet fail under local tools, files, formats, and failure modes. We propose SkillMe, a workflow-situated skill evolution framework that adapts agent skills through repeated use in matched workflow recurrences. A Publisher induces and validates an initial skill from a successful execution, while Executors use the skill in agent-native sessions and provide trace-grounded feedback. SkillMe activates the matched skill on demand as workspace files, allowing agents to inspect and adopt guidance progressively without permanently installing it as a local skill. On the 71-task skill-needed subset of PinchBench with OpenClaw, SkillMe improves the overall mean score of all eight Executors over their paired no-skill baselines, with mean gains of +25.7 points. Comparisons show that evolved task-scoped skills are more stable than static skills and more reliable than broadly reusable skills. SkillMe thus provides an agent-native path to evolve reusable skills into effective workflow guidance through situated use.</abstract>
    <identifier type="citekey">lu-etal-2026-skillme</identifier>
    <location>
        <url>https://aclanthology.org/2026.emnlp-main.564/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>12951</start>
            <end>12973</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T SkillMe: Evolving Agent Skills for Recurring Personal Workflows
%A Lu, Xiuqi
%A Zhang, Jie
%A Liu, Yuanfu
%A Yan, Lewen
%A Zhi, Yutong
%A Shao, Jing
%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 lu-etal-2026-skillme
%X Skills help LLM agents reuse procedural knowledge in recurring workflows, but broad transferability alone does not guarantee workflow fit: a reusable skill may be semantically relevant yet fail under local tools, files, formats, and failure modes. We propose SkillMe, a workflow-situated skill evolution framework that adapts agent skills through repeated use in matched workflow recurrences. A Publisher induces and validates an initial skill from a successful execution, while Executors use the skill in agent-native sessions and provide trace-grounded feedback. SkillMe activates the matched skill on demand as workspace files, allowing agents to inspect and adopt guidance progressively without permanently installing it as a local skill. On the 71-task skill-needed subset of PinchBench with OpenClaw, SkillMe improves the overall mean score of all eight Executors over their paired no-skill baselines, with mean gains of +25.7 points. Comparisons show that evolved task-scoped skills are more stable than static skills and more reliable than broadly reusable skills. SkillMe thus provides an agent-native path to evolve reusable skills into effective workflow guidance through situated use.
%U https://aclanthology.org/2026.emnlp-main.564/
%P 12951-12973

Markdown (Informal)

[SkillMe: Evolving Agent Skills for Recurring Personal Workflows](https://aclanthology.org/2026.emnlp-main.564/) (Lu et al., EMNLP 2026)

• SkillMe: Evolving Agent Skills for Recurring Personal Workflows (Lu et al., EMNLP 2026)

ACL

• Xiuqi Lu, Jie Zhang, Yuanfu Liu, Lewen Yan, Yutong Zhi, and Jing Shao. 2026. SkillMe: Evolving Agent Skills for Recurring Personal Workflows. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 12951–12973, Budapest, Hungary. Association for Computational Linguistics.