skillX

Use when operating SkillX full-loop experiments after an inner-loop run completes, especially to audit result validity, classify failures, decide required/recommended reruns, prepare targeted rerun manifests, or determine whether it is safe to proceed to outer-loop optimization.

An overview of the python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.

An overview of the python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.

An overview of the core model API of SeisBench, a Python framework for training and applying machine learning algorithms to seismic data. It is useful for annotating waveforms using pretrained SOTA ML models, for tasks like phase picking, earthquake detection, waveform denoising and depth estimation. For any waveform, you can manipulate it into an obspy stream object and it will work seamlessly with seisbench models.

This is a summary the advantages and disadvantages of earthquake event detection and phase picking methods, shared by leading seismology researchers at the 2025 Earthquake Catalog Workshop. Use it when you have a seismic phase picking task at hand.

Clean messy tabular datasets with deduplication, missing value imputation, outlier handling, and text processing. Use when dealing with dirty data that has duplicates, nulls, or inconsistent formatting.

Difference-in-Differences causal analysis to identify demographic drivers of behavioral changes with p-value significance testing. Use for event effects, A/B testing, or policy evaluation.

Engineer dataset features before ML or Causal Inference. Methods include encoding categorical variables, scaling numerics, creating interactions, and selecting relevant features.

Detect anomalies in time series data using Prophet Framework (Meta), which frames the seasonality, trend holiday effect and other needed regressors into its model, to identify unusual surges or slumps in trends. This is a general methodology analyst can use for understanding what changes of their tracking metrics are manifesting anomalies pattern.