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STRING Protein Interaction

string-database

K-Dense-AI/claude-scientific-skills

Query the STRING REST API to retrieve protein-protein interaction networks, map identifiers, visualize evidence-weighted edges, and run GO/KEGG/Pfam enrichment for systems biology analysis.

UMAP Manifold Reduction

K-Dense-AI/claude-scientific-skills

UMAP-Learn delivers fast nonlinear dimensionality reduction for visualization, clustering prep, and feature engineering; it mirrors scikit-learn APIs and supports supervised/semi-supervised embeddings with tunable parameters.

Vaex Big Data

K-Dense-AI/claude-scientific-skills

Vaex processes massive tabular datasets (billions of rows) with lazy, out-of-core DataFrames, fast aggregations, visualization, and ML pipelines without fitting data into memory, ideal for CSV/Arrow/Parquet workflows.

Phylogenetics Pipeline Toolkit

K-Dense-AI/claude-scientific-skills

Build and analyze phylogenetic trees with MAFFT, IQ-TREE 2, FastTree, and visualize via ETE3 or FigTree, covering alignment, trimming, ML inference, and microbial/viral evolutionary studies.

Data Visualization Tools

data-visualization

sickn33/antigravity-awesome-skills

Provides charting, mapping, and dashboard techniques to turn datasets into clear insights for sharing across teams.

Family Health Analyzer

family-health-analyzer

sickn33/antigravity-awesome-skills

Analyzes family medical history to surface hereditary risk patterns, validate data, and generate personalized prevention guidance plus visual reports for informed lifestyle or screening decisions.

PyGraphistry GPU Visualizer

sickn33/antigravity-awesome-skills

PyGraphistry is a Python interface to the Graphistry platform for GPU-accelerated graph visualization, letting analysts render millions of nodes/edges in interactive dashboards to surface anomalies and relationships quickly.

Umap Visualization Tool

sickn33/antigravity-awesome-skills

Umap provides uniform manifold approximation for dimension reduction and visualization, helping data scientists explore high-dimensional embeddings through interpretable 2D/3D layouts.

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