The growth of agent skills has transformed how agentic systems are built, evaluated, and deployed. As skill libraries continue to scale, rigorous evaluation becomes critical to ensuring their utility, quality, and safety in real-world applications. Consequently, the field is undergoing an emerging paradigm shift from isolated skill creation to automated, evaluation-driven skill evolution. In this survey, we systematically examine the landscape of skill evolution and evaluation beyond foundational skill creation. We categorize evolution into four distinct paradigms, spanning execution feedback, trajectory distillation, compression, and reinforcement learning, showing how each element contributes to improving skill utility and reliability. We also provide an analysis of six skill-centric benchmark categories, identifying structural gaps in benchmark coverage, trade-offs, and metric richness to advance skill research. Finally, we identify open directions for building skill ecosystems that are generalizable, efficient, and verifiably safe. The project URL is https://github.com/Cassie07/AgentSkill_Survey

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides open access to macromolecular structures and offers an extensive structural keyword search infrastructure. However, the current RCSB PDB APIs do not support natural-language semantic search. To overcome this challenge, we propose RAD-PROTEIN, a novel multi-source knowledge-enriched Retrieval-Augmented Protein structure search system that answers user natural-language queries by jointly leveraging heterogeneous structural literature and metadata. In addition, we introduce the first benchmark for text-based protein-structure search evaluation, comprising 454 expert-verified queries. RAD-PROTEIN substantially outperforms baselines, achieving up to +19.8% Recall@1 and +16.1% in MRR with hybrid retrieval and multi-source knowledge enrichment. Comprehensive ablation studies further confirm the robustness of our proposed approach. Finally, we develop an interpretable interface to return top-ranked structures with LLM-generated explanations, enabling efficient human verification and downstream scientific analysis. Code: https://github.com/Cassie07/RAD_PROTEIN