Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
108 skills found
Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.
Production-ready reinforcement learning using Stable Baselines3. Train agents, design custom environments, implement training callbacks, and optimize workflows with a scikit-learn-style API.
Bayesian modeling and probabilistic programming with PyMC. Build hierarchical models, perform MCMC sampling (NUTS), variational inference, and conduct rigorous model comparison using LOO and WAIC.
Access AI-ready datasets, benchmarks, and molecular oracles for drug discovery, including ADME, toxicity, DTI, and molecular generation tasks.
Python skill for high-performance storage of chunked N-dimensional arrays using Zarr, supporting cloud storage (S3/GCS), parallel I/O, and integration with NumPy, Dask, and Xarray.
Preprocessing and cleaning astronomical light curves using Lightkurve. Tools for outlier removal, flattening, trend detrending, and quality flag handling for time-series analysis.
Comprehensive toolkit for graph creation, network analysis, and visualization in Python. Ideal for analyzing relationships, centrality, community detection, and synthetic network generation across diverse research domains.
Analyze periodic signals in unevenly sampled astronomical time series data using the Lomb-Scargle periodogram method with the lightkurve library.
Data Analysis Specialist for EDA, statistical modeling, SQL queries, and Python-based visualization. Turn raw datasets into actionable insights through rigorous quantitative methods.
Statistical modeling and econometrics library for Python. Performs OLS, GLM, mixed models, ARIMA, diagnostics, and inference for rigorous scientific analysis.
Comprehensive Python healthcare AI toolkit for clinical data processing, medical coding translation, and developing deep learning models like RETAIN and Transformers for EHR, physiological signals, and clinical prediction tasks.
Train and manage neural networks in distributed E2B sandboxes using the Flow Nexus platform, supporting custom architectures like Transformers, LSTMs, and GANs.