SkillBrowser lets the agent identify a capability gap during reasoning, formulate a targeted query, and retrieve the procedural skill it needs. It avoids loading a large skill catalogue up front.
Across five models and six SRA-Bench subsets, with 26,262 candidate skills, it beats the same-backend static retriever in 23 of 30 settings, with one tie.
Evidence & limits. Submitted to ACL Rolling Review, not accepted. Author-reported results; useful skills sometimes go unrequested, the catalogue is static, and latency rises. Both query formulation and exposure timing change, so the study does not isolate selective loading alone. Its timing comparison excludes the baseline’s precomputed retrieval.
Evaluate skill discovery during the task, including missed invocations and end-to-end latency. A smaller prompt is useful only if the right procedure still arrives when needed.