Info-Skill: A State-Conditional Information Bottleneck for Skill-Augmented Agents

Skill-augmented language agents often retrieve procedural skills from a persistent library, but most existing methods inject each retrieved skill as full text. This ignores a basic property of procedural guidance: the useful part of a skill depends on the current decision state. As a result, full-text prompting wastes context, exposes the policy to irrelevant instructions, and leaves library maintenance without a reliable measure of skill value. We propose Info-Skill, a predictive-rate framework that unifies state-conditional skill compression with skill-library evolution. At decision time, Info-Skill encodes the retrieved skill with a state-conditional stochastic bottleneck and optimizes outcome prediction under a variational rate penalty, so the same skill can preserve different information at different stages of an interaction. The resulting code is mapped to a fixed number of continuous prefix embeddings, allowing a frozen backbone with lightweight adaptation to use retrieved skills without full-text prompting. At the library level, we use held-out predictive fidelity gain, variational rate, and redundancy to score candidate additions and existing entries, and paired rollout tests to validate each update. This gives a single information-based view of both how a retrieved skill should be used and which skills a library should retain. Experiments on embodied interaction, search-based question answering, and web navigation show that Info-Skill improves the strongest benchmark-level metrics by 2.0% on average while reducing the context consumed by retrieved skills by 92.6%, delivering the strongest aggregate performance across all three backbones.