# SkillContrast: Difference-Guided Text Selection for Agent Skill Reranking

arXiv: https://arxiv.org/abs/2610.11650 · HTML: https://mlsanity.com/paper/2610.11650/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 8 Oct 2026.

## Builds on (most cited first, 6 of 6)

- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [SkillRouter: Skill Routing for LLM Agents at Scale](https://mlsanity.com/paper/2603.22455/) (2026)
- [SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agents](https://mlsanity.com/paper/2605.05726/) (2026)
- [Skill Is Not Document: Query-Conditioned Compatibility for LLM Agent Skill Routing](https://mlsanity.com/paper/2606.03565/) (2026)
- [Right Family, Wrong Skill: Evaluating Risk Exposure in Agent Skill Retrieval](https://mlsanity.com/paper/2606.10388/) (2026)
- [SkillSight: Calibrating Generic Content Bias for Skill Retrieval](https://mlsanity.com/paper/2607.18785/) (2026)

## Abstract (arXiv)

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.