Skill UI

This comprehensive guide helps authors evaluate if their hydrology or water-science manuscript is a suitable fit for the Water Resources Research journal. It mandates that contributions must be quantitative, generalizable advances, not single-basin case studies. The tool emphasizes rigorous benchmarking, adherence to AGU house styles, and compliance with open data/code deposition policies.

This skill provides an in-depth, entity-level scorecard for clinical or biomedical Named Entity Recognition (NER) models. It calculates precision, recall, and F1 scores (both strict and partial match modes) using a user-supplied gold corpus. Key features include per-label error breakdown, a confusion matrix, and detailed False Negative/False Positive analysis, essential for debugging model performance in highly sensitive medical domains.

Guides users to select a local, on-device Personally Identifiable Information (PII) detection model from a committed registry. It filters models based on language, required runtime environment (e.g., PyTorch, MLX), and size constraints. The process emphasizes building an offline shortlist and mandates rigorous pre-deployment recall validation and performance benchmarking to ensure secure, privacy-preserving deployment.