SkillPager: Query-Adaptive Intra-Skill Navigation via Semantic Node Retrieval
Skill-based LLM agents increasingly rely on long procedural documents, but full-document prompting wastes tokens and dilutes information critical to execution. We study this setting as intra-skill retrieval, where the goal is to select a minimal, execution-sufficient context from a known skill document given a query. We present SkillPager, a two-stage framework that parses each Markdown skill into typed semantic nodes offline and leverages Maximal Marginal Relevance (MMR) to perform global, query-conditioned node selection online. On a benchmark of 395 skills and 1,975 queries, SkillPager achieves 78.89% LLM-judged context sufficiency, compared to 82.23% for the exhaustive full-document b...
| Authors | Zicai Cui, Zihan Guo, Weiwen Liu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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