mlsanityData to 14 Oct 2026

Right Family, Wrong Skill: Evaluating Risk Exposure in Agent Skill Retrieval

arXiv 2606.10388 ↗First posted 9 Jun 2026cs.IR cs.AIcited by 4

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Abstract (arXiv)

A skill can match a task's topic while conflicting with its resource, procedure, or output requirements. We study this as same-capability risk-exposure retrieval and introduce SameCapRisk-Bench: 890 units and 1,314 query cases across five mechanisms and twelve conflict types. Each unit pairs a skill that meets a query requirement with a same-capability skill that violates it, with evidence for that distinction. The evaluation tests source-task requirements and two kinds of paired queries that reverse which skill fits: changing the requested evidence role or exact output interface while holding the skills fixed. To capture both retrieval success and conflicting exposure, Recall tracks helpful hits, harmful sibling rate (HSR) tracks exposure of the conflicting sibling, and CleanHit requires a helpful hit without that exposure. Four public skill retrievers expose conflicting siblings at HSR@3 of 0.737-0.881 on source-task contracts, compared with 0.099-0.12 on controlled source-role queries. Across the fixed mixture of 1,235 held-out queries, their Recall@3 is 0.903-0.944 and HSR@3 is 0.344-0.393. The conflicting sibling ranks first on 24.8-29.2% of source-task queries for these four retrievers. We also examine what the reranker receives: truncating long skills can remove the passages where the two skills' contracts differ. With the same BGE top-20 candidates, increasing the reranker's input budget from 512 to 4,096 tokens reduces source-task sibling-first errors by 8.27 pp, with an uncertain CleanHit gain.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 47 references that are in the map. First posted on arXiv 9 Jun 2026.

Builds on

22 of 47 references.

References · 22

  1. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  2. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  3. 2017Adversarial Examples for Evaluating Reading Comprehension Systemscited by 571
  4. 2026SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Taskscited by 315
  5. 2023ToolQA: A Dataset for LLM Question Answering with External Toolscited by 274
  6. 2023TheoremQA: A Theorem-driven Question Answering datasetcited by 192
  7. 2026SWE-Skills-Bench: Do Agent Skills Actually Help in Real-World Software Engineering?cited by 82
  8. 2024MedCalc-Bench: Evaluating Large Language Models for Medical Calculationscited by 70
  9. 2026How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settingscited by 65
  10. 2026SkillRouter: Skill Routing for LLM Agents at Scalecited by 61
  11. 2024Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agentscited by 56
  12. 2026Graph-of-Skills: Dependency-Aware Structural Retrieval for Massive Agent Skillscited by 55
10 more
  1. 2026Skill Retrieval Augmentation for Agentic AIcited by 44
  2. 2024ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrievalcited by 41
  3. 2026SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agentscited by 39
  4. 2026MalSkillBench: A Runtime-Verified Benchmark of Malicious Agent Skillscited by 19
  5. 2026AgentTrap: Measuring Runtime Trust Failures in Third-Party Agent Skillscited by 12
  6. 2026Group of Skills: Group-Structured Skill Retrieval for Agent Skill Librariescited by 11
  7. 2026SkillRAE: Agent Skill-Based Context Compilation for Retrieval-Augmented Executioncited by 8
  8. 2026Skill Is Not Document: Query-Conditioned Compatibility for LLM Agent Skill Routingcited by 7
  9. 2026SkillGuard: A Permission-Centric Framework for Agent Skill Securitycited by 6
  10. 2026OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agentscited by 4

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4 papers.