Data Analysis

A comprehensive spreadsheet skill for creating, editing, and analyzing Excel files (.xlsx). It provides Python code patterns using pandas and openpyxl, plus a recalculation script that uses LibreOffice to compute formula values and detect errors.

This skill calculates key financial ratios and metrics from financial statement data for investment analysis

This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions

A comprehensive reference guide for calculating portfolio risk metrics. Provides Python code patterns for Value at Risk (VaR), Conditional VaR, Sharpe ratio, Sortino ratio, maximum drawdown, and other risk measurements used in portfolio management and risk monitoring systems.

A comprehensive skill for working with Excel spreadsheets (.xlsx, .xlsm, .csv, .tsv) including creating new files with formulas and formatting, reading/analyzing data with pandas, editing existing files while preserving formulas, and recalculating formula values using LibreOffice. Includes industry-standard financial modeling conventions.

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

A comprehensive tool for accessing Federal Reserve Economic Data (FRED), providing 800,000+ economic time series including GDP, unemployment, inflation, and interest rates. Includes a Python client library with automatic retry/caching and extensive API documentation for economic research and financial analysis.

A comprehensive spreadsheet toolkit for working with Excel and CSV files. It enables creating and editing spreadsheets with formulas, formatting, and data analysis using openpyxl and pandas, with automatic formula recalculation through LibreOffice integration.

This skill guides users through statistical hypothesis testing (t-tests, ANOVA, chi-square, regression) with proper assumption checking, effect size calculation, and APA-formatted reporting. It includes Python utilities for diagnostic visualizations and comprehensive reference documentation covering Bayesian methods, power analysis, and test selection.

This skill enables access to the NCBI Gene Expression Omnibus (GEO), a public repository containing over 264,000 gene expression studies. It provides tools to search for datasets, download microarray and RNA-seq data, parse SOFT/matrix files, and perform expression analysis including differential expression and meta-analysis across studies.

A comprehensive reference skill for Vaex, a high-performance Python library for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Covers DataFrame operations, data loading, filtering, aggregations, machine learning pipelines, visualization, and performance optimization strategies.

A comprehensive reference skill for the statsmodels Python library, covering statistical modeling techniques including linear regression, generalized linear models, discrete choice models, time series analysis, and statistical diagnostics. Provides code examples, best practices, and detailed explanations for econometrics and rigorous statistical inference.

A comprehensive documentation skill for scvi-tools, a Python framework for probabilistic deep generative models in single-cell genomics. It provides guidance on models for RNA-seq, ATAC-seq, multimodal data integration, spatial transcriptomics, and specialized modalities like methylation and cytometry analysis.

A comprehensive reference skill for scikit-bio, a Python bioinformatics library. Provides documentation and code examples for biological sequence manipulation, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination analysis (PCoA, CCA), statistical tests (PERMANOVA, ANOSIM), and microbiome data processing with FASTA/BIOM file format support.

This skill provides a comprehensive toolkit for analyzing single-cell RNA-seq data using the scanpy library. It enables quality control, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, marker gene identification, cell type annotation, and publication-quality visualizations.

Pysam is a documentation skill for genomic data analysis using Python. It provides comprehensive reference guides for reading and writing sequencing alignment files (SAM/BAM/CRAM), genetic variant files (VCF/BCF), and sequence files (FASTA/FASTQ), along with practical code examples for bioinformatics workflows like coverage analysis and variant filtering.

PyOpenMS is a comprehensive documentation skill for computational mass spectrometry using Python. It provides guidance for proteomics workflows including feature detection, peptide identification, protein quantification, and LC-MS/MS data processing pipelines.

A comprehensive Bayesian modeling skill built on PyMC 5.x that enables probabilistic programming with MCMC sampling (NUTS), variational inference, and hierarchical models. Includes diagnostic utilities for convergence checking, model comparison using LOO/WAIC, and templates for common model patterns like linear regression and multilevel models.

This skill provides comprehensive guidance for using the matchms Python library for mass spectrometry data processing and analysis. It covers spectral similarity calculations, compound identification from spectral libraries, data filtering, and format conversion for metabolomics research workflows.

Hypogenic is a framework for automated scientific hypothesis generation using large language models. It can generate testable hypotheses from data alone (HypoGeniC), combine literature insights with empirical patterns (HypoRefine), or use both approaches together (Union methods). The skill provides configuration templates and documentation for accelerating research discovery across domains like deception detection, content analysis, and predictive modeling.

A comprehensive bioinformatics skill that provides a unified Python interface to 40+ biological databases including UniProt, KEGG, ChEMBL, and Reactome. It enables protein analysis, pathway discovery, compound searches, sequence similarity searches, and cross-database identifier mapping for scientific research workflows.

This skill provides comprehensive documentation and reference material for working with AnnData, a Python package for handling annotated data matrices used in single-cell genomics. It covers creating, reading, writing, and manipulating AnnData objects, along with best practices for memory management and integration with the scverse ecosystem (Scanpy, Muon, PyTorch).

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

A sleep analysis skill that processes sleep tracking data from local JSON files to identify sleep patterns, assess quality, and detect potential issues like insomnia or sleep apnea risk. It provides personalized recommendations for improving sleep hygiene including bedtime routines, environment optimization, and lifestyle adjustments.

This skill provides a comprehensive framework for analyzing sexual health data, including IIEF-5 erectile dysfunction scoring, STD screening schedules, contraception effectiveness tracking, and correlations with medications, chronic diseases, mental health, nutrition, and exercise. It is purely informational and designed to help organize health data for discussion with medical professionals.

This skill analyzes rehabilitation training data to track recovery progress, identify pain patterns, and evaluate goal achievement. It reads local JSON files containing exercise logs, functional assessments, and pain diaries to generate comprehensive progress reports with trend analysis and personalized recommendations.

This skill analyzes occupational health data to assess workplace health risks including sedentary behavior, screen time exposure, and ergonomic issues. It reads local health tracker JSON files, calculates risk scores across multiple dimensions, and generates comprehensive markdown reports with personalized recommendations for improving workplace health.