Tiledbvcf

If you're dealing with genomic variant data at scale, this wraps TileDB's sparse array technology to make VCF/BCF storage and queries actually manageable. The big win is incremental sample addition without expensive merging operations, which matters when your cohort keeps growing. Built on 1-based coordinates like VCF standard, works with cloud storage out of the box. One gotcha: it only handles single-sample VCFs and needs index files, so you'll need to split multi-sample files first. Best for population genomics workflows where you're constantly querying specific regions across hundreds or thousands of samples, not for quick one-off analyses on a handful of files.

skills.sh installs: 2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill tiledbvcf --agent claude-code

Installs into .claude/skills of the current project.

Files

TileDB-VCF

When to use

Use for cohort variant storage, incremental sample ingestion, interval queries, and exporting subsets for downstream genomics. TileDB-VCF stores records; it does not perform joint variant calling, association testing, normalization, or population structure adjustment. Dataset size alone does not determine whether cloud execution is appropriate: benchmark the intended sample, region, and attribute workload.

This skill targets released TileDB-VCF 0.40.3. Native Python/CLI checks used two synthetic single-sample VCFs on macOS ARM64. Cloud client 0.14.4 contracts were checked against its released wheel and mocked dispatch; hosted queries and jobs were not executed. See installation and verification notes.

Install and verify the native stack

The tiledbvcf Python distribution is not published on PyPI at this review. The official tiledb Conda channel supplies tiledbvcf-py 0.40.3 for macOS ARM64, macOS x86-64, and Linux x86-64, with Python 3.9-3.12 builds. tiledb on PyPI is TileDB-Py and does not install TileDB-VCF. Native Apple Silicon no longer requires forcing CONDA_SUBDIR=osx-64.

SKILL.md on GitHub