Skill UI

Comprehensive Python integration for OMERO, an open-source platform designed for managing, visualizing, and analyzing complex microscopy datasets. This skill allows users to programmatically access images, retrieve structured datasets, manage annotations (ROIs, tags), perform deep pixel analysis, and execute batch processing for high-content screening and advanced bioimaging workflows.

PathML is a comprehensive Python toolkit designed for advanced computational pathology workflows. It facilitates the end-to-end analysis of whole-slide pathology images (WSI) from over 160 proprietary formats. Key functionalities include image preprocessing (stain normalization, tissue detection), constructing spatial cell/tissue graphs, and training deep learning models (e.g., HoVer-Net). It is highly specialized for analyzing multiparametric data from technologies like CODEX and Vectra.

This comprehensive toolkit facilitates the entire phylogenetic analysis workflow, from sequence alignment to tree inference. It integrates industry-standard tools like MAFFT for multiple sequence alignment, TrimAl for alignment trimming, and powerful maximum likelihood methods using IQ-TREE 2 or FastTree. Ideal for molecular genomics, tracing viral phylodynamics, studying protein evolution, or conducting molecular clock analysis. Built using Python for seamless integration.

PyDESeq2 is a robust Python implementation designed for differential gene expression analysis using bulk RNA-seq count data. It provides a comprehensive, end-to-end bioinformatics workflow, guiding users from data loading and design specification (including multi-factor effects) through the core DESeq2 fitting process. The tool performs Wald tests, applies FDR correction, and offers advanced features like LFC shrinkage, making it essential for integrating complex genomics analysis into Python-based pipelines.

A comprehensive guide to probabilistic programming using PyMC. Learn how to build, fit, validate, and compare complex Bayesian models, including hierarchical structures and time series. The guide covers the full workflow: data preparation, MCMC sampling (NUTS), diagnostic checking (R-hat, ESS), posterior predictive checks, and making predictions for uncertainty quantification.

PyOpenMS is a comprehensive platform providing Python bindings to the OpenMS library. It is designed for advanced mass spectrometry data analysis, supporting entire proteomics and metabolomics workflows. Key capabilities include handling diverse file formats (mzML, mzXML), signal processing, robust feature detection, and precise peptide/protein identification. It is ideal for quantitative biological research, allowing users to process complex LC-MS/MS pipelines.

A comprehensive Python toolkit for analyzing single-cell RNA-seq data. This skill guides users through the entire workflow, including quality control (QC), normalization, dimensionality reduction (PCA, UMAP, t-SNE), robust cell clustering, and marker gene identification. Ideal for exploratory biological research and generating publication-quality visualizations.

SHAP (SHapley Additive exPlanations) provides a unified framework based on Shapley values to interpret complex machine learning models. It calculates feature attributions, enabling users to understand exactly why a model made a specific prediction. This skill supports generating various visualizations (e.g., waterfall, beeswarm, force plots) for feature importance, model debugging, bias analysis, and implementing robust explainable AI across diverse model types like XGBoost, PyTorch, and TensorFlow.

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.

Utilize Bright Data's Scraping Browser with Playwright or Puppeteer to reliably scrape structured data from JavaScript-rendered pages, Single Page Applications (SPAs), and anti-bot-protected sites. This tool manages proxy routing, CAPTCHA solving, and fingerprint management, ensuring robust data collection from complex web environments.

A comprehensive workflow designed to build deep revenue analytics dashboards using exported Clari data. It calculates key metrics such as forecast accuracy, pipeline coverage ratios, and tracks representative performance changes. Ideal for financial analysts and sales managers needing to assess revenue health and forecast reliability.

A robust scheduler designed for orchestrating large-scale, multi-stage Machine Learning experiments on remote GPU servers. It manages complex workflows such as multi-seed grid sweeps, wave transitions, and sequential job chains (e.g., teacher-student distillation). Key features include OOM-aware retries, stale screen cleanup, and race condition prevention, making it ideal when basic single-run scheduling is insufficient for complex batch processing.