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SkillContrast introduces a training-free method for optimizing agent skill retrieval by focusing on textual differences rather than shared instructions, effectively reducing input token usage while improving safety and accuracy. This approach addresses a critical bottleneck in agentic workflows where standard query-based selection often fails to distinguish between similar but conditionally distinct skills. By retaining only the divergent context, the technique enhances reranker performance without requiring additional model training or excessive computational overhead.
SkillContrast: Difference-Guided Text Selection for Agent Skill Reranking
arXiv:2610.11650v1 Announce Type: cross 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.
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.
Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
arXiv:2610.11650 [cs.IR]
(or arXiv:2610.11650v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2610.11650 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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