Skill UI

Analyzes dietary and nutrition records to identify trends and deficiencies, compares intake against RDA/DRI, correlates with exercise, sleep, and chronic disease data, and generates prioritized personalized nutrition improvements and habit suggestions.

Analyzes IIEF-5 scores, STD screening data, contraception effectiveness, and sexual activity logs to highlight risk factors, interventions, and treatment impact—ideal for clinicians or coaches generating structured health summaries.

Analyzes TCM constitution data to classify body types, evaluate traits, track trends, and generate personalized wellness advice by correlating nutrition, exercise, sleep and chronic-disease indicators.

Query the PrimeKG precision medicine knowledge graph to explore genes, drugs, diseases, phenotypes, and their relationships, supporting drug discovery, repurposing, and phenotype analysis through programmatic scripts.

The Stock Analyzer Skill provides comprehensive technical analysis for stocks and ETFs. It utilizes industry-standard indicators like RSI, MACD, and Bollinger Bands to generate actionable trading signals. Users can perform advanced technical comparisons, monitor portfolio strength, and identify chart patterns through natural language queries, eliminating the need for manual indicator calculation.

A comprehensive tool for generating publication-quality figures essential for Machine Learning and AI academic papers. It supports two distinct workflows: generating complex system architecture and workflow diagrams using AI (Gemini), and creating precise, data-driven visualizations (line charts, heatmaps, etc.) using standard libraries like matplotlib and seaborn. Use it when preparing any figure for a conference submission.

Transforms raw input into intelligent visualizations by selecting from 26 chart types, extracting required parameters from reference specs, and running a Node.js generator to return the final chart image URL and payload.

Generates a single formula-driven Excel dashboard from CSV/tabular data, complete with KPI cards, charts, sparklines, and conditional formatting so analysts can instantly review metrics and share an executive-ready .xlsx output.

Guides agents through validation, dry-run MCP tool usage, and monitors-as-code YAML generation so Monte Carlo monitors can be established via CLI or CI/CD without directly creating assets.

Creates a SQL validation notebook for dbt changes (PR or local) that compares before/after states, resolves schemas, and launches the notebook via Monte Carlo’s bridge interface.

This skill enhances research query formulation by translating vague, colloquial, or generalized descriptions into precise, standard academic terminology. When initial search terms are broad (e.g., "conjugal happiness"), this technique suggests multiple plausible academic synonyms (e.g., "marital satisfaction," "relationship quality"). By improving the precision of the search query and incorporating structured constraints (like keywords, years, or sample sizes), it significantly boosts the relevance and effectiveness of academic paper retrieval.

Arboreto infers gene regulatory networks from bulk or single-cell RNA-seq data using scalable GRNBoost2 and GENIE3 algorithms, letting researchers filter transcription-factor targets, run distributed Dask jobs, and export ranked links for downstream analysis.