Skill UI

Molfeat is a comprehensive Python library designed for molecular featurization. It converts chemical structures (SMILES/RDKit) into numerical feature vectors, supporting over 100 featurizers like ECFP, MACCS, and deep embeddings (ChemBERTa). It unifies featurization pipelines, making it ideal for QSAR modeling, virtual screening, and integrating into deep learning ML workflows.

NetworkX is a powerful Python library used for creating, manipulating, and analyzing complex networks and graphs. It supports graph structures (directed/undirected, multi-edges) and provides extensive algorithms for tasks such as finding shortest paths, calculating centrality measures (e.g., PageRank), detecting communities, and simulating various network types (e.g., scale-free, small-world). Ideal for social science, biology, and transportation network modeling.

This skill provides a comprehensive Python API integration for the OMERO platform, an open-source solution for managing and analyzing large-scale microscopy datasets. Users can programmatically access images, retrieve structured data, analyze raw pixel data, manage complex annotations (ROIs), and execute batch processing workflows essential for high-content screening and advanced biological research.

A systematic framework designed for conducting rigorous peer reviews of scientific manuscripts and grant proposals. It provides a structured, checklist-based evaluation covering methodology rigor, statistical validity, reporting compliance (e.g., CONSORT, STROBE), and assesses all sections from abstract to results. Ideal for academic publishing, research quality assessment, and providing detailed, constructive feedback.

PyMC is a comprehensive Python library for probabilistic programming and Bayesian modeling. It enables users to build, fit, and validate complex statistical models, including hierarchical structures, time series, and regression models. It provides powerful tools for MCMC sampling (NUTS) and variational inference, making it essential for rigorous uncertainty quantification and model comparison (LOO/WAIC).

This skill compiles comprehensive external evidence required for drafting high-quality scientific manuscripts or research briefs. It goes beyond simple link lists, targeting verified, unique academic references (e.g., 60 references) and generating a structured research packet. Use it when you need literature reviews, evidence supporting a claim, or a deep dive into scientific background, methodology, or limitations.

TorchDrug is a comprehensive PyTorch-based ML toolbox designed for molecular and protein science. It leverages Graph Neural Networks (GNNs) to tackle complex tasks including molecular property prediction, protein structure modeling, drug-target binding prediction, knowledge graph reasoning, and de novo molecular generation. It provides robust capabilities for accelerating drug discovery research.

UMAP (Uniform Manifold Approximation and Projection) is a powerful technique for non-linear dimensionality reduction. This skill enables the creation of scalable, structure-preserving embeddings (2D/3D) for visualization, clustering preprocessing, and advanced machine learning feature engineering. It supports various metrics (e.g., Euclidean, Cosine) and supervised learning workflows.

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.

This tool collects comprehensive diagnostic data from ClickHouse system tables. It gathers crucial metrics, query logs, merge statuses, and disk usage information. It is essential for troubleshooting performance bottlenecks, preparing detailed support tickets, or investigating complex database infrastructure issues.

This comprehensive guide teaches users how to set up a ClickHouse database environment. It covers creating a MergeTree table, inserting structured event data using TypeScript, and executing essential analytical queries (e.g., counting, grouping, unique user metrics). Ideal for beginners learning OLAP databases, testing connections, or mastering core data warehousing concepts.