Agent skill · data analytics · k-dense-ai
tiledbvcf
Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
Why this skill is useful
Provides domain-specific knowledge and commands for efficient genomic variant data storage and retrieval that the AI wouldn't reliably generate on its own.
What it needs
Requires conda, docker, mamba, numpy, pandas, pyarrow, tiledb, tiledb-py, tiledbvcf-py installed locally. About 7k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
TileDB-VCF Overview TileDB-VCF is a high-performance C++ library with Python and CLI interfaces for efficient storage and retrieval of genomic variant-call data. Built on TileDB's sparse array technology, it enables scalable ingestion of VCF/BCF files, incremental sample addition without expensive merging operations, and efficient parallel queries of variant data stored locally or in the cloud. When to Use This Skill This skill should be used when: Learning TileDB-VCF concepts and workflows Prototyping genomics analyses and pipelines Working with small-to-medium datasets (< 1000 samples) Need incremental addition of new samples to existing datasets Require efficient querying of specific genomic regions across many samples Working with cloud-stored variant data (S3, Azure, GCS) Need to export subsets of large VCF datasets Building variant databases for cohort studies Educational projects and method development Performance is critical for variant data operations Quick Start Installation Preferred Method: Conda/Mamba Alternative: Docker Images Basic Examples Create and populate a dataset: Query variant data: Export to VCF: Core Capabilities 1. Dataset Creation and Ingestion Create TileDB-VCF datasets and incrementally ingest variant data from multiple VCF/BCF files. This is appropriate for building population genomics databases and cohort studies. Requirements: Single-sample VCFs only: Multi-sample VCFs are not supported Index files required: VCF/BCF files must have indexes (.csi or .tbi) Common operations: Create new datasets with optimized array schemas Ingest single or multiple VCF/BCF files in parallel Add new samples incrementally without re-processing existing data Configure memory usage and compression settings Handle various VCF formats and INFO/FORMAT fields Resume interrupted ingestion processes Validate data integrity during ingestion 2. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/tiledbvcf