[
    {
        "id": "osp-15064",
        "type": "article-journal",
        "title": "SkillContrast: Difference-Guided Text Selection for Agent Skill Reranking",
        "author": [
            {
                "family": "Ding",
                "given": "Jiandong"
            },
            {
                "family": "Ji",
                "given": "Honglei"
            },
            {
                "family": "Liu",
                "given": "Ming"
            },
            {
                "family": "Duan",
                "given": "Tao"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/skillcontrast-difference-guided-text-selection-for-agent-skill-reranking",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Similar agent skills can share instructions but differ in their conditions of use. Query-based text selection may retain shared instructions and omit these distinctions. We introduce SkillContrast, a training-free selector that compares retrieved skills and retains their differing text with local context for a pretrained reranker. On 1,235 requests from SameCapRisk-Bench, it yields 54-72 more clean hits (requests that retrieve a helpful skill without its marked risky sibling) than TF-IDF query selection at identical per-candidate input lengths, across 2 retrievers and 2 reranker sizes. Length-matched component replacements identify differing text as the main contributor in the primary setting, with smaller, mixed context effects. Relative to full skill bodies, SkillContrast uses 51.1-58.8% fewer model-input tokens, with 10-18 fewer clean hits at 0.6B and matching or higher observed clean-hit counts at 4B. Candidate-relative differences thus complement query relevance in selecting compact reranking inputs."
    }
]