Skill UI

PyDESeq2 brings the DESeq2 differential expression workflow to Python, letting users load bulk RNA-seq counts, specify design formulas, fit DESeq2 models, run Wald tests with FDR correction, optionally apply apeGLM shrinkage, and export gene-level statistics within pandas/AnnData pipelines.

PyHealth is a modular Python toolkit designed for building deep learning pipelines using complex medical data, including EHR records, physiological signals, and medical imaging. It provides a standardized 5-stage workflow: Dataset → Task → Model → Trainer → Metrics. This allows users to train sophisticated models for clinical prediction (e.g., mortality, readmission, drug recommendation) while handling medical code mapping (ICD, ATC, RxNorm) and ensuring proper data splitting for rigorous research.

scikit-learn is the industry-standard Python library for classical machine learning. It provides comprehensive tools for building, evaluating, and deploying ML models. Use it for classification, regression, clustering, dimensionality reduction, data preprocessing, and hyperparameter tuning in production pipelines.

Provide a comprehensive toolkit for RNA velocity inference in single-cell RNA-seq data, modeling unspliced/spliced kinetics to infer differentiation directions, latent time, and driver genes while complementing Scanpy pipelines.

A comprehensive skill for leveraging massive pre-trained transformer models across diverse AI domains, including Natural Language Processing (NLP), Computer Vision (CV), and Audio. Use this skill for advanced tasks such as text generation, classification, question answering, summarization, object detection, and model fine-tuning on proprietary datasets. It provides streamlined pipelines and granular control for deployment.

Vaex is a high-performance Python library for lazy out-of-core DataFrames that process billions of rows without RAM. Use it to analyze massive CSV/HDF5/Arrow/Parquet tables, compute fast statistics, visualize big data, and build scikit-learn pipelines.

Access and query 78 public databases covering diverse fields including biomedical, physical sciences, earth science, and economics. This skill identifies the relevant database(s) for user queries (e.g., genes, compounds, patents, climate data, financial indicators), executes structured REST API calls, and returns raw JSON results, providing a comprehensive data retrieval pipeline.

A comprehensive guide to setting up robust CI/CD pipelines for Algolia indexing. This includes running unit and integration tests against live indices, validating index settings against defined configurations, and implementing automated reindexing upon deployment (e.g., triggering on version tags). It ensures the stability and data consistency of your search service.

A comprehensive tool for managing annotation datasets, supporting full CRUD operations, label management, and advanced export functionality. It specifically handles COCO format export and facilitates uploading datasets to platforms like Kaggle, streamlining the data pipeline for AI model development.

Provides comprehensive programmatic control over Apify's core data infrastructure, including Datasets, Key-Value Stores, and Request Queues. This skill allows users to perform CRUD operations on various storage types, manage data persistence, and orchestrate complex, multi-stage data pipelines (e.g., scrape -> transform -> export). Ideal for building robust, end-to-end web scraping and data processing automation workflows.

This comprehensive guide outlines essential security best practices for users operating within the Apify ecosystem. Learn how to properly manage API tokens, implement environment separation (dev/staging/prod), secure proxy credentials, and sanitize sensitive data (PII) before storage. Essential for hardening data pipelines and ensuring compliance.

A comprehensive workflow for managing employee lifecycles within BambooHR. This skill enables CRUD operations (Create, Read, Update, Delete) on employee records, synchronizing directory data to external systems, generating custom reports, and interacting with structured tables like job history and compensation. Ideal for building robust HR data pipelines and integrating HR data into external applications.