Recent advances in agentic visualization have enabled the translation of natural language into executable scientific visualization (SciVis) workflows. While general-purpose coding agents show strong capabilities, they often lack the tool-specific expertise required for SciVis tasks. In this work, we present SciVisAgentSkills, a collection of reusable agent skills that augment coding agents for scientific data analysis and visualization by encoding environment assumptions, tool usage patterns, and domain heuristics across scientific tools such as ParaView, napari, VMD, and TTK. We evaluate these skills on Codex and Claude Code using SciVisAgentBench, a benchmark of 108 expert-designed multi-step tasks. Results show that agent skills improve mean task scores across the evaluated suites, with token-efficiency benefits that depend on the agent harness and tool setting. These findings highlight the importance of structured procedural knowledge for enabling reliable, long-horizon SciVis workflows, while also showing that skills should be studied alongside the execution harness that loads and applies them. The skills are available at https://github.com/KuangshiAi/SciVisAgentSkills.

翻译：近期，智能体可视化技术的最新进展已实现将自然语言转换为可执行的科学可视化工作流。尽管通用编码智能体展现出强大能力，但它们在科学可视化任务中常缺乏工具特定专业知识。本研究提出SciVisAgentSkills——一组可复用的智能体技能，通过编码环境假设、工具使用模式及领域启发式知识，增强编码智能体在ParaView、napari、VMD、TTK等科学工具中的数据分析与可视化能力。我们利用包含108个专家设计多步任务的基准测试SciVisAgentBench，在Codex和Claude Code上对这些技能进行评估。结果表明，智能体技能显著提升了各评估套件的平均任务得分，且其令牌效率优势取决于智能体框架与工具设置。这些发现揭示了结构化程序性知识对实现可靠、长周期科学可视化工作流的重要性，同时表明技能需与加载和应用它的执行框架协同研究。相关技能代码已开源至https://github.com/KuangshiAi/SciVisAgentSkills。