Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
Performs placebo-in-time sensitivity analysis to validate causal claims. Use when checking model robustness, verifying lack of pre-intervention effects, or ensuring observed effects are not spurious.
Query a molecule's biochemical significance and roles in biology and chemistry using BioT5 multi-modal model. Use this skill when: (1) Understanding a molecule's biological roles and functions, (2) Describing a molecule's chemical significance and applications, (3) Getting natural language explanations of molecular properties, (4) Summarizing what a molecule is used for or its metabolic relevance.
Identify the IUPAC name of a molecule using BioT5 question answering model. Use this skill when: (1) User wants to find the IUPAC name of a molecule, (2) User asks "What is the IUPAC name?" or "What's the systematic name?", (3) User provides a SMILES string and wants the IUPAC nomenclature.
Analyze protein mutations by retrieving protein data, explaining mutation effects, predicting protein structure, and visualizing results. Use this skill when the user asks about protein mutations, wants to understand mutation effects, or needs to analyze genetic variants. Triggers on phrases like "analyze mutation", "explain mutation", "what does this mutation do", "protein variant analysis".
Query UniProt database for protein sequences, metadata, and search by criteria. Use this skill when: (1) Looking up protein information by UniProt accession ID, (2) Searching proteins by gene name, organism, function, or disease, (3) Retrieving comprehensive protein metadata including domains, PTMs, and annotations.
Predict protein function and properties from amino acid sequence using BioT5. Use this skill when: (1) You have a protein sequence and want to understand its biological function, (2) You need to identify enzyme activity, pathway involvement, or molecular interactions, (3) You want a concise description of protein properties from sequence alone.
Retrieve proteins with similar structures, sequences, or from the same family. Use this skill when: (1) Finding similar proteins or homologs, (2) Searching for proteins with similar 3D structure, (3) Performing sequence similarity search, (4) Discovering proteins in the same family.
Use this skill when a task involves Geneformer workflows, especially TranscriptomeTokenizer input preparation, tokenized `.dataset` generation, cell or gene classification with `Classifier`, embedding extraction with `EmbExtractor`, and in silico perturbation analysis with `InSilicoPerturber`.
This skill enables Claude to forecast future values based on historical time series data. It analyzes time-dependent data to identify trends, seasonality, and other patterns. Use this skill when the user asks to predict future values of a time series, analyze trends in data over time, or requires insights into time-dependent data. Trigger terms include "forecast," "predict," "time series analysis," "future values," and requests involving temporal data.
This skill empowers Claude to perform regression analysis and modeling using the regression-analysis-tool plugin. It analyzes datasets, generates appropriate regression models (linear, polynomial, etc.), validates the models, and provides performance metrics. Use this skill when the user explicitly requests regression analysis, prediction based on data, or mentions terms like "linear regression," "polynomial regression," "regression model," or "predictive modeling." This skill is also helpful when the user needs to understand the relationship between variables in a dataset.
This skill enables Claude to forecast future values based on historical time series data. It analyzes time-dependent data to identify trends, seasonality, and other patterns. Use this skill when the user asks to predict future values of a time series, analyze trends in data over time, or requires insights into time-dependent data. Trigger terms include "forecast," "predict," "time series analysis," "future values," and requests involving temporal data.
Process this skill enables AI assistant to forecast future values based on historical time series data. it analyzes time-dependent data to identify trends, seasonality, and other patterns. use this skill when the user asks to predict future values of a time ser... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Cohort Analysis Creator - Auto-activating skill for Data Analytics. Triggers on: cohort analysis creator, cohort analysis creator Part of the Data Analytics skill category.
This skill empowers Claude to identify anomalies and outliers within datasets. It leverages the anomaly-detection-system plugin to analyze data, apply appropriate machine learning algorithms, and highlight unusual data points. Use this skill when the user requests anomaly detection, outlier analysis, or identification of unusual patterns in data. Trigger this skill when the user mentions "anomaly detection," "outlier analysis," "unusual data," or requests insights into data irregularities.
This skill empowers Claude to perform regression analysis and modeling using the regression-analysis-tool plugin. It analyzes datasets, generates appropriate regression models (linear, polynomial, etc.), validates the models, and provides performance metrics. Use this skill when the user explicitly requests regression analysis, prediction based on data, or mentions terms like "linear regression," "polynomial regression," "regression model," or "predictive modeling." This skill is also helpful when the user needs to understand the relationship between variables in a dataset.
This skill empowers Claude to identify anomalies and outliers within datasets. It leverages the anomaly-detection-system plugin to analyze data, apply appropriate machine learning algorithms, and highlight unusual data points. Use this skill when the user requests anomaly detection, outlier analysis, or identification of unusual patterns in data. Trigger this skill when the user mentions "anomaly detection," "outlier analysis," "unusual data," or requests insights into data irregularities.
This skill empowers Claude to identify anomalies and outliers within datasets. It leverages the anomaly-detection-system plugin to analyze data, apply appropriate machine learning algorithms, and highlight unusual data points. Use this skill when the user requests anomaly detection, outlier analysis, or identification of unusual patterns in data. Trigger this skill when the user mentions "anomaly detection," "outlier analysis," "unusual data," or requests insights into data irregularities.
This skill empowers Claude to perform regression analysis and modeling using the regression-analysis-tool plugin. It analyzes datasets, generates appropriate regression models (linear, polynomial, etc.), validates the models, and provides performance metrics. Use this skill when the user explicitly requests regression analysis, prediction based on data, or mentions terms like "linear regression," "polynomial regression," "regression model," or "predictive modeling." This skill is also helpful when the user needs to understand the relationship between variables in a dataset.
This skill enables Claude to forecast future values based on historical time series data. It analyzes time-dependent data to identify trends, seasonality, and other patterns. Use this skill when the user asks to predict future values of a time series, analyze trends in data over time, or requires insights into time-dependent data. Trigger terms include "forecast," "predict," "time series analysis," "future values," and requests involving temporal data.
Comprehensive diagnostic analysis of time series data. Use when users provide CSV time series data and want to understand its characteristics before forecasting - stationarity, seasonality, trend, forecastability, and transform recommendations.
Identify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions. Computes posterior probabilities for causal variants, links variants to genes via L2G predictions, annotates functional consequences, and suggests validation strategies. Use when asked to fine-map GWAS loci, prioritize causal variants, identify credible sets, or link GWAS signals to causal genes.
Comprehensive systems biology and pathway analysis using multiple pathway databases (Reactome, KEGG, WikiPathways, Pathway Commons, BioModels). Performs pathway enrichment, protein-pathway mapping, keyword searches, and systems-level analysis. Use when analyzing gene sets, exploring biological pathways, or investigating systems-level biology.
