Update skill counts and add TileDB-VCF to repository documentation

- Update skill count badges and descriptions from 146 to 147 skills
- Add TileDB-VCF to genomic tools list in bioinformatics section
- Add variant database management use case for TileDB-VCF
- Add comprehensive TileDB-VCF entry to docs/scientific-skills.md

README.md

@@ -1,11 +1,11 @@
 # Claude Scientific Skills
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE.md)
-[![Skills](https://img.shields.io/badge/Skills-146-brightgreen.svg)](#whats-included)
+[![Skills](https://img.shields.io/badge/Skills-147-brightgreen.svg)](#whats-included)
 [![Agent Skills](https://img.shields.io/badge/Standard-Agent_Skills-blueviolet.svg)](https://agentskills.io/)
 [![Works with](https://img.shields.io/badge/Works_with-Cursor_|_Claude_Code_|_Codex-blue.svg)](#getting-started)
 
-A comprehensive collection of **146+ ready-to-use scientific and research skills** (now including financial/SEC research, U.S. Treasury fiscal data, OFR Hedge Fund Monitor, and Alpha Vantage market data) for any AI agent that supports the open [Agent Skills](https://agentskills.io/) standard, created by [K-Dense](https://k-dense.ai). Works with **Cursor, Claude Code, Codex, and more**. Transform your AI agent into a research assistant capable of executing complex multi-step scientific workflows across biology, chemistry, medicine, and beyond.
+A comprehensive collection of **147+ ready-to-use scientific and research skills** (now including financial/SEC research, U.S. Treasury fiscal data, OFR Hedge Fund Monitor, and Alpha Vantage market data) for any AI agent that supports the open [Agent Skills](https://agentskills.io/) standard, created by [K-Dense](https://k-dense.ai). Works with **Cursor, Claude Code, Codex, and more**. Transform your AI agent into a research assistant capable of executing complex multi-step scientific workflows across biology, chemistry, medicine, and beyond.
 
 **Looking for the full AI co-scientist experience?** Try [K-Dense Web](https://k-dense.ai) for 200+ skills, cloud compute, and publication-ready outputs.
 
@@ -68,7 +68,7 @@ These skills enable your AI agent to seamlessly work with specialized scientific
 
 ## 📦 What's Included
 
-This repository provides **146 scientific and research skills** organized into the following categories:
+This repository provides **147 scientific and research skills** organized into the following categories:
 
 - **30+ Scientific & Financial Databases** - Direct API access to OpenAlex, PubMed, bioRxiv, ChEMBL, UniProt, COSMIC, ClinicalTrials.gov, SEC EDGAR, U.S. Treasury Fiscal Data, Alpha Vantage, and more
 - **55+ Python Packages** - RDKit, Scanpy, PyTorch Lightning, scikit-learn, BioPython, BioServices, PennyLane, Qiskit, and others
@@ -341,6 +341,7 @@ networks, and search GEO for similar patterns.
 - **Sequence Analysis**: Process DNA/RNA/protein sequences with BioPython and pysam
 - **Single-Cell Analysis**: Analyze 10X Genomics data with Scanpy, identify cell types, infer GRNs with Arboreto
 - **Variant Annotation**: Annotate VCF files with Ensembl VEP, query ClinVar for pathogenicity
+- **Variant Database Management**: Build scalable VCF databases with TileDB-VCF for incremental sample addition, efficient population-scale queries, and compressed storage of genomic variant data
 - **Gene Discovery**: Query NCBI Gene, UniProt, and Ensembl for comprehensive gene information
 - **Network Analysis**: Identify protein-protein interactions via STRING, map to pathways (KEGG, Reactome)
 
@@ -371,14 +372,14 @@ networks, and search GEO for similar patterns.
 
 ## 📚 Available Skills
 
-This repository contains **143 scientific and research skills** organized across multiple domains. Each skill provides comprehensive documentation, code examples, and best practices for working with scientific libraries, databases, and tools.
+This repository contains **144 scientific and research skills** organized across multiple domains. Each skill provides comprehensive documentation, code examples, and best practices for working with scientific libraries, databases, and tools.
 
 ### Skill Categories
 
 #### 🧬 **Bioinformatics & Genomics** (16+ skills)
 - Sequence analysis: BioPython, pysam, scikit-bio, BioServices
 - Single-cell analysis: Scanpy, AnnData, scvi-tools, Arboreto, Cellxgene Census
-- Genomic tools: gget, geniml, gtars, deepTools, FlowIO, Zarr
+- Genomic tools: gget, geniml, gtars, deepTools, FlowIO, Zarr, TileDB-VCF
 - Phylogenetics: ETE Toolkit
 
 #### 🧪 **Cheminformatics & Drug Discovery** (11+ skills)

docs/scientific-skills.md

@@ -69,6 +69,7 @@
 - **geniml** - Genomic interval machine learning toolkit providing unsupervised methods for building ML models on BED files. Key capabilities include Region2Vec (word2vec-style embeddings of genomic regions and region sets using tokenization and neural language modeling), BEDspace (joint embeddings of regions and metadata labels using StarSpace for cross-modal queries), scEmbed (Region2Vec applied to single-cell ATAC-seq data generating cell-level embeddings for clustering and annotation with scanpy integration), consensus peak building (four statistical methods CC/CCF/ML/HMM for creating reference universes from BED collections), and comprehensive utilities (BBClient for BED caching, BEDshift for genomic randomization preserving context, evaluation metrics for embedding quality, Text2BedNN for neural search backends). Part of BEDbase ecosystem. Supports Python API and CLI workflows, pre-trained models on Hugging Face, and integration with gtars for tokenization. Use cases: region similarity searches, dimension reduction of chromatin accessibility data, scATAC-seq clustering and cell-type annotation, metadata-aware genomic queries, universe construction for standardized references, and any ML task requiring genomic region feature vectors
 - **gtars** - High-performance Rust toolkit for genomic interval analysis providing specialized tools for overlap detection using IGD (Integrated Genome Database) indexing, coverage track generation (uniwig module for WIG/BigWig formats), genomic tokenization for machine learning applications (TreeTokenizer for deep learning models), reference sequence management (refget protocol compliance), fragment processing for single-cell genomics (barcode-based splitting and cluster analysis), and fragment scoring against reference datasets. Offers Python bindings with NumPy integration, command-line tools (gtars-cli), and Rust library. Key modules include: tokenizers (convert genomic regions to ML tokens), overlaprs (efficient overlap computation), uniwig (ATAC-seq/ChIP-seq/RNA-seq coverage profiles), refget (GA4GH-compliant sequence digests), bbcache (BEDbase.org integration), scoring (fragment enrichment metrics), and fragsplit (single-cell fragment manipulation). Supports parallel processing, memory-mapped files, streaming for large datasets, and serves as foundation for geniml genomic ML package. Ideal for genomic ML preprocessing, regulatory element analysis, variant annotation, chromatin accessibility profiling, and computational genomics workflows
 - **pysam** - Read, write, and manipulate genomic data files (SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences) with pileup analysis, coverage calculations, and bioinformatics workflows
+- **TileDB-VCF** - High-performance C++ library with Python and CLI interfaces for efficient storage and retrieval of genomic variant-call data using TileDB multidimensional sparse array technology. Enables scalable VCF/BCF ingestion with incremental sample addition, compressed storage, parallel queries across genomic regions and samples, and export capabilities for population genomics workflows. Key features include: memory-efficient queries, cloud storage integration (S3, Azure, GCS), and CLI tools for dataset creation, sample ingestion, data export, and statistics. Supports building variant databases for large cohorts, population-scale genomics studies, and association analysis. Use cases: population genomics databases, cohort studies, variant discovery workflows, genomic data warehousing, and scaling to enterprise-level analysis with TileDB-Cloud platform
 - **PyDESeq2** - Python implementation of the DESeq2 differential gene expression analysis method for bulk RNA-seq data. Provides statistical methods for determining differential expression between experimental conditions using negative binomial generalized linear models. Key features include: size factor estimation for library size normalization, dispersion estimation and shrinkage, hypothesis testing with Wald test or likelihood ratio test, multiple testing correction (Benjamini-Hochberg FDR), results filtering and ranking, and integration with pandas DataFrames. Handles complex experimental designs, batch effects, and replicates. Produces fold-change estimates, p-values, and adjusted p-values for each gene. Use cases: identifying differentially expressed genes between conditions, RNA-seq experiment analysis, biomarker discovery, and gene expression studies requiring rigorous statistical analysis
 - **Scanpy** - Comprehensive Python toolkit for single-cell RNA-seq data analysis built on AnnData. Provides end-to-end workflows for preprocessing (quality control, normalization, log transformation), dimensionality reduction (PCA, UMAP, t-SNE, ForceAtlas2), clustering (Leiden, Louvain, hierarchical clustering), marker gene identification, trajectory inference (PAGA, diffusion maps), and visualization. Key features include: efficient handling of large datasets (millions of cells) using sparse matrices, integration with scvi-tools for advanced analysis, support for multi-modal data (RNA+ATAC, CITE-seq), batch correction methods, and publication-quality plotting functions. Includes extensive documentation, tutorials, and integration with other single-cell tools. Supports GPU acceleration for certain operations. Use cases: single-cell RNA-seq analysis, cell-type identification, trajectory analysis, batch correction, and comprehensive single-cell genomics workflows
 - **scvi-tools** - Probabilistic deep learning models for single-cell omics analysis. PyTorch-based framework providing variational autoencoders (VAEs) for dimensionality reduction, batch correction, differential expression, and data integration across modalities. Includes 25+ models: scVI/scANVI (RNA-seq integration and cell type annotation), totalVI (CITE-seq protein+RNA), MultiVI (multiome RNA+ATAC integration), PeakVI (ATAC-seq analysis), DestVI/Stereoscope/Tangram (spatial transcriptomics deconvolution), MethylVI (methylation), CytoVI (flow/mass cytometry), VeloVI (RNA velocity), contrastiveVI (perturbation studies), and Solo (doublet detection). Supports seamless integration with Scanpy/AnnData ecosystem, GPU acceleration, reference mapping (scArches), and probabilistic differential expression with uncertainty quantification