Agent skills do not make models smarter； they help models avoid wasting context while finding the right text.
i.e. agent skills are not a universal replacement for search or stronger reasoning.
They matter most when the knowledge base becomes too large for an agent to browse efficiently， and the simplest 1-level layout worked best overall.
When an agent can search 1 book well， progressive disclosure adds little， but it helps across large libraries.
A single routing layer preserves context better than deeper skill hierarchies across large document libraries.
The team compared raw navigation， flat and deeper skill layouts， and a standard retriever across several agent setups.
On 1 book， strong agents already searched well， so the flat skill brought almost no accuracy gain.
With 20 books， English open-question accuracy rose from 0.257 under raw navigation to 0.462 with the flat skill.
Strong agents gain little from prepared skill indexes until the corpus becomes too large to navigate.
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- arxiv. org/abs/2607.17598
Title： "Is Progressive Disclosure All You Need for Long-Context Agents？"