Skill UI

This skill provides comprehensive, real-time research lookup by intelligently routing queries to the optimal backend. It utilizes fast web search for general market data, advanced APIs for deep synthesis research, and dedicated services for academic papers and DOI searches. Use it for literature verification, background context gathering, and technical documentation retrieval.

This skill provides a systematic framework for evaluating the rigor of scientific claims, research methodology, and evidence quality. It guides users through assessing experimental design validity, identifying various types of biases (e.g., selection, cognitive), and applying established grading frameworks like GRADE or Cochrane Risk of Bias. Ideal for systematic reviews, peer review, and critical analysis of scientific literature.

scikit-bio is a comprehensive Python library designed for advanced bioinformatics and biological data analysis. It provides robust capabilities for sequence manipulation (DNA, RNA, Protein), performing complex sequence alignments (pairwise and multiple), constructing and analyzing phylogenetic trees, and calculating diverse ecological metrics. It is essential for microbiome analysis, evolutionary studies, and general biological data processing, supporting standard formats like FASTA, Newick, and BIOM.

scikit-survival is a comprehensive Python library built on scikit-learn, designed for rigorous time-to-event analysis. It specializes in handling censored data (right, left, or interval-censored) and provides multiple advanced modeling techniques, including Cox proportional hazards models, Random Survival Forests, Gradient Boosting, and Survival SVMs. Users can evaluate model performance using metrics like the Concordance Index and Brier Score, making it ideal for clinical, reliability, and survival research.

SHAP (SHapley Additive exPlanations) provides a unified, robust framework for understanding complex machine learning models. It calculates feature importance using Shapley values from game theory, allowing users to interpret individual predictions, analyze model bias, visualize feature interactions, and debug model behavior. Applicable across deep learning, tree-based, and black-box models.

Statsmodels is a robust Python library providing comprehensive tools for advanced statistical analysis. It supports a wide range of models, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM like Poisson and Logistic), Weighted Least Squares (WLS), and complex time series forecasting (ARIMA). Use this skill for rigorous econometrics, detailed diagnostic testing, and generating publication-ready statistical inference.

SymPy is a powerful Python library designed for exact symbolic computation, allowing users to perform advanced mathematical operations without resorting to floating-point approximations. It provides comprehensive tools for symbolic algebra, differentiation, integration, solving complex equations (linear and non-linear), and handling linear algebra. It is essential for scientific computing, physics modeling, and advanced mathematical research, supporting multiple output formats like LaTeX.

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.

This skill provides comprehensive access to 78 public REST APIs, covering biomedical science, chemistry, materials science, physics, and economics. Users can query vast datasets for genes, proteins, compounds, clinical trials, patents, and environmental indicators. It is an indispensable tool for deep academic research, complex data analysis, and cross-disciplinary scientific investigation.

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 skill provides a robust solution for monitoring critical forecast changes within Clari. Since Clari lacks native real-time webhooks for all data, this system implements change detection by polling periodic data exports and comparing current snapshots against historical records. It calculates the percentage change and can trigger immediate alerts via platforms like Slack, ensuring users are always aware of significant shifts in revenue forecasts or sales pipelines.

A comprehensive guide to reducing expenses on ClickHouse Cloud. Learn advanced techniques for storage optimization (compression, TTL), compute resource tuning (scaling, thread limits), and query efficiency improvements. Use this when managing data budgets or analyzing high ClickHouse billing charges.