The temptation of data-science automation has been around for ten years, but LLM Agents that can truly replace data scientists to "read dirty data, build features, produce reports" still lack a public ruler. AgenticDataBench (arXiv:2607.01647), just released by Tsinghua University and other institutions, tries to fill this ruler. The biggest difference from traditional evaluation is the introduction of "data-science skills" as the intermediate granularity: 433 operational patterns are extracted from Stack Overflow's large-scale task solutions — missing-value imputation, time-series resampling, anomaly validation, etc. — and then skill-aligned hierarchical clustering removes redundancy, finally composing 344 tasks, 97 datasets, 27.3 GB of data, covering 15 vertical domains, including 5 real B2B business flows from a top fintech company. Another highlight is the task synthesis pipeline — for domains lacking real data, the authors use LLM to reverse-synthesize tasks and standard answers around "skill combinations", avoiding the benchmark being overly biased toward hot domains like finance and e-commerce. The evaluation results aren't disclosed in the abstract, but the authors have open-sourced the testing platform and GitHub repo, giving the community a reproducible entry point. This path is especially critical for domestic Agent frameworks — previously everyone could only self-promote on the few example questions they prepared at home, now there's finally a third-party "exam" to compete on. [Opinion] AgenticDataBench's real value may not lie in "which model ranks first", but in making the abstract phrase "data-science skills" into an enumerable, testable, teachable object. Once the skill library is public, teams building vertical Agents can reverse-select training data and target weak spots — this may be the first piece of the LLM for Data Science puzzle heading toward engineering.