Data Science & ML Claudeスキル

ReportLab PDF Generation

16

Automates the creation of professional PDF documents, reports, and invoices using the robust ReportLab Python toolkit.

Zarr Python

16

Manages large-scale N-dimensional arrays with chunking and compression for high-performance scientific computing and cloud storage.

GWAS Catalog Explorer

16

Queries the NHGRI-EBI GWAS Catalog for genetic variants, SNP-trait associations, and summary statistics to support genetic epidemiology research.

DeepChem Cheminformatics

16

Applies machine learning to chemistry, biology, and materials science to predict molecular properties and design new compounds.

Portfolio Optimization

16

Optimizes financial computations and portfolio risk metrics by implementing high-performance Python C extensions for large-scale numerical data.

MCMC Sampling with Stan

16

Implements Bayesian Markov Chain Monte Carlo sampling workflows using RStan for complex hierarchical modeling and statistical inference.

Scikit-bio Bioinformatics

16

Performs advanced biological data analysis including sequence manipulation, phylogenetic tree construction, and microbial diversity metrics.

Exploratory Data Analysis Toolkit

16

Automates comprehensive statistical analysis and visual profiling for diverse datasets to uncover hidden patterns, anomalies, and actionable insights.

Multi Source Data Merger

16

Merges heterogeneous data sources into unified datasets using field mappings and priority-based conflict resolution.

Statsmodels Statistical Analysis

16

Performs rigorous statistical modeling, econometric analysis, and hypothesis testing using Python's statsmodels library.

Scientific Hypothesis Generator

16

Generates testable, evidence-based scientific hypotheses and experimental designs from observations or literature.

CSV Data Summarizer

16

Automatically analyzes CSV files to generate comprehensive statistical summaries and tailored visualizations without requiring user prompts.

ChEMBL Database for Drug Discovery

16

Queries ChEMBL's vast database of bioactive molecules and drug discovery data for medicinal chemistry research.

Gemini CLI Integration

16

Integrates Google Gemini CLI to provide Claude with real-time web search, deep codebase analysis, and multi-model code generation capabilities.

Raman Spectroscopy Peak Fitting

16

Analyzes and fits peaks in Raman spectroscopy data using physically-constrained models like Lorentzian, Gaussian, and Voigt functions.

FlowIO Flow Cytometry Handler

16

Parses and manipulates Flow Cytometry Standard (FCS) files to extract event data as NumPy arrays and manage metadata.

Modernize Scientific Stack

16

Upgrades legacy Python 2 scientific computing code and analysis pipelines to modern Python 3 standards using contemporary libraries like NumPy and pandas.

Biomni

16

Executes autonomous multi-step biomedical research tasks including genomics analysis, drug discovery, and clinical interpretation.

pyOpenMS Proteomics Toolkit

16

Processes mass spectrometry data for proteomics and metabolomics analysis using the pyOpenMS library.

Video Processing

16

Provides structured guidance for video analysis, motion detection, and temporal event tracking using computer vision techniques.

RStan to PyStan Conversion

16

Translates Stan statistical models and inference code from R (RStan) to Python (PyStan 3.x) while handling API mappings and output differences.

MTEB Leaderboard Navigator

16

Retrieves and verifies temporally-accurate data from dynamic machine learning leaderboards to ensure model rankings and benchmark results are current and valid.

GPT-2 Code Golf Implementation

16

Implements neural network inference for transformer architectures within extreme code size constraints and minimal environments.

ClinVar Genomic Database

16

Queries and interprets NCBI ClinVar data to evaluate human genetic variants and their clinical significance.

scvi-tools

16

Empowers single-cell omics analysis with deep generative models for dimensionality reduction, batch correction, and multimodal data integration.

Build Cython Extensions

16

Provides comprehensive guidance for compiling Cython extensions and resolving version-specific NumPy compatibility issues.

SHAP Model Interpretability

16

Explains machine learning model predictions and feature importance using Shapley values to provide transparent and actionable AI insights.

DiffDock Molecular Docking

16

Predicts high-accuracy 3D binding poses for protein-ligand complexes using diffusion-based deep learning models.

PyHealth

16

Develops, tests, and deploys healthcare-specific machine learning models using electronic health records, clinical prediction tasks, and medical coding systems.

DNA Assembly

16

Designs and validates DNA assembly strategies, including Golden Gate and Gibson assembly, with precise requirements for Type IIS restriction enzyme primers.