# Skills Know Their Neighbors: Cluster-Contrastive Capability Pages for Skill Retrieval

arXiv: https://arxiv.org/abs/2608.04482 · HTML: https://mlsanity.com/paper/2608.04482/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Agent skill learning and evolution, one of 16 directions in LLM agents and tool use.
- First posted on arXiv 5 Aug 2026.

## Placed in

- LLM agents and tool use / [Agent skill learning and evolution](https://mlsanity.com/agents/agent-skill-learning-and-evolution/)

## Built on by (most cited first, 3 of 3)

- [SkillGate: Training In-Policy Skill Selection in Long-Horizon Agents](https://mlsanity.com/paper/2608.18852/) (2026)
- [What Does a Skill Actually Do? Estimands and Evaluation Validity for Tool and Skill Use in LLM Agents: A Critical Review](https://mlsanity.com/paper/2609.33153/) (2026)
- [What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agents](https://mlsanity.com/paper/2609.39989/) (2026)

## Abstract (arXiv)

As skill libraries grow, large language model agents must retrieve reusable skills from candidates that often share the same topic and vocabulary but implement different capabilities. Retrieval is limited not only by the scorer but also by the text being scored: a document may describe what a skill does without stating which similar requests should be routed elsewhere. We formalize a skill's capability as its executable region, the set of queries it can solve, and view its document as a lossy observation of that region. This view exposes a document-imposed component of retrieval error that cannot be removed by improving the retriever alone. We therefore propose Capability Pages, cluster-contrastive skill representations containing a positive trigger Tpos, a negative boundary Tneg, and a discriminative body B. An offline compiler compares neighboring skills to write these fields. At inference time, the index uses Tpos and B for candidate recall, while the router uses Tneg to reject confusable alternatives. On SRA-Bench, which contains 26,262 skills and 5,400 questions from six datasets, Capability Pages improve Recall@10 for all five tested retrievers, with a mean gain of 2.94 points. Adding Tneg to candidate cards improves end-to-end task success by 3.62 points on average across four executors and six datasets. A transfer evaluation on Chinese SSL-SkillDiscovery reaches 73.07% MRR@50 using the same encoder across conditions. Capability Pages require no modification to the online models; they improve routing by rewriting the offline skill library.