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PyMC Bayesian Modeling

pymc-bayesian-modeling

K-Dense-AI/claude-scientific-skills

Structured workflow for building hierarchical Bayesian models with PyMC 5+, covering data prep, NUTS sampling, diagnostics, posterior checks, and predictive inference.

Therapeutics Data Commons

K-Dense-AI/claude-scientific-skills

PyTDC offers curated AI-ready datasets, standardized benchmarks, and scaffold-aware splits across ADME, toxicity, interaction, and generation tasks to support therapeutic ML and pharmacological prediction workflows.

Scanpy Single-Cell Pipeline

K-Dense-AI/claude-scientific-skills

Scanpy provides a scalable Python pipeline for single-cell RNA-seq, covering QC, normalization, dimensionality reduction, clustering, marker discovery, annotation, trajectory analysis, and publication-ready visualizations through AnnData.

Scientific Critical Thinking

scientific-critical-thinking

K-Dense-AI/claude-scientific-skills

Assess scientific claims by reviewing study design, statistical validity, and bias using GRADE and Cochrane frameworks, helping researchers or reviewers grade evidence quality and spot methodological flaws before drawing conclusions.

Scikit Learn ML Guide

K-Dense-AI/claude-scientific-skills

Comprehensive guidance for building classical machine learning workflows with scikit-learn, spanning supervised and unsupervised algorithms, preprocessing, evaluation, tuning, and production-ready pipelines for structured or text data.

Survival Analysis Toolkit

scikit-survival

K-Dense-AI/claude-scientific-skills

Comprehensive scikit-survival guidance for handling censored data, building Cox, ensemble, and SVM survival models, and evaluating time-to-event predictions with concordance, AUC, and Brier scores, making it ideal for clinical or reliability studies in Python.

Seaborn Statistical Visualization

K-Dense-AI/claude-scientific-skills

Seaborn builds on matplotlib to deliver dataset-aware statistical plots with attractive defaults, making it easy to explore distributions, variable relationships, and categorical comparisons in Python.

Guided Statistical Analysis

statistical-analysis

K-Dense-AI/claude-scientific-skills

Guides hypothesis testing, regression, correlation, and Bayesian analyses with automated assumption checks, diagnostics, power planning, and APA-style reporting so researchers can choose proper tests, interpret effect sizes, and document academic findings.

Statsmodels Statistical Modeling

K-Dense-AI/claude-scientific-skills

Statsmodels is a Python library for detailed regression and time-series models, giving econometrics-grade inference, diagnostics, residual analysis, model comparison, and forecasting tools when you need publication-ready tables and rigorous testing.

TileDB VCF Storage

K-Dense-AI/claude-scientific-skills

TileDB-VCF lets you ingest, store, query, and export large genomic VCF/BCF datasets using TileDB’s sparse arrays, supporting incremental sample growth, parallel regions queries, and cloud-local interoperability for population genomics workflows.

US Treasury Fiscal Data

K-Dense-AI/claude-scientific-skills

Query the open U.S. Treasury Fiscal Data API for federal financial statistics, including national debt, government spending, treasury interest rates, exchange rates, and savings bonds without needing an API key.

Zarr Python Arrays

K-Dense-AI/claude-scientific-skills

Handles chunked, compressed N-D arrays in Python for cloud-friendly workflows; integrates seamlessly with NumPy, Dask, and Xarray to enable parallel I/O, sharding, and persistence to S3/GCS for large-scale scientific pipelines.

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