Acta Automatica Sinica Artifact Evaluation
Structures anonymous, reproducible code and data artifacts for submissions to Acta Automatica Sinica to maximize manuscript credibility during peer review.
The aas-artifact-evaluation skill guides researchers through preparing transparent, anonymous, and reproducible code and data packages for manuscripts submitted to Acta Automatica Sinica (《自动化学报》). It clarifies the journal's peer-review context, double-blind review requirements, and AI disclosure rules while providing standardized directory layouts for control simulations, robotics experiments, and pattern recognition models. Researchers can systematically address non-public industrial datasets, lock down execution environments, and map manuscript figures directly to runnable scripts for rigorous review.