# tiledbvcf

This skill stores genomic variants in TileDB-VCF, ingests VCF/BCF files, and queries data by region or sample. Use it for cohort workflows.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1900 installs · ★ 54652
- Author: K-Dense-AI
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/tiledbvcf/SKILL.md
- Updated: 2026-10-19
- HTML: https://affpapa.org/en/nevibecode/skills/tiledbvcf

## What it does

The skill guides an agent through indexed single-sample VCF/BCF ingestion into TileDB-VCF, incremental cohort updates, and queries by genomic region or sample. It can stream query results, read allele statistics, run QC-related workflows, and export selected data to VCF/BCF. Region queries account for overlapping records and the coordinate conventions used by BED input and statistics.

Local use requires a native TileDB-VCF installation, Python 3.9-3.12, and `bcftools` for compressing and indexing input files. Each input must contain one sample, be coordinate-sorted, and have a matching `.csi` or `.tbi` index. Without native TileDB-VCF, the skill cannot perform these storage and query operations. TileDB Cloud additionally needs its Python client, an account token, and storage permissions.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill tiledbvcf -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/tiledbvcf/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/tiledbvcf/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill tiledbvcf -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/tiledbvcf/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill tiledbvcf -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/tiledbvcf/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install K-Dense-AI/scientific-agent-skills tiledbvcf
```

2. **Manually: put the files in place**
- File: `.github/skills/tiledbvcf/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill tiledbvcf -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/K-Dense-AI/scientific-agent-skills --path skills/tiledbvcf
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/tiledbvcf/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill tiledbvcf -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/tiledbvcf/SKILL.md

## When to use it
- ingest VCF/BCF files into a cohort
- append a sample to an existing dataset
- query variants across a genomic region
- stream results in bounded batches
- export a selected subset as VCF/BCF

## Example prompts
- Load `sample1.vcf.gz` into the `cohort` dataset and check `chr1:1-100`.
- Add `sample2.vcf.gz` to the existing cohort without creating the dataset again.
- Read variants for samples `S1` and `S2` as a stream, then calculate allele statistics.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: fcb05b8d9435dd70.

BEGIN THIRD-PARTY FILE fcb05b8d9435dd70
````text
---
name: tiledbvcf
description: Stores and retrieves genomic variant calls with TileDB-VCF. Use for indexed single-sample VCF/BCF ingestion, incremental cohorts, region and sample queries, streaming results, allele statistics, QC, and VCF/BCF export locally or through TileDB Cloud.
license: MIT license
compatibility: Requires a native TileDB-VCF installation and Python 3.9-3.12 for the reviewed Conda builds. bcftools compresses/indexes input fixtures. Installation and remote storage need network access; TileDB Cloud needs its Python client, account token, and storage permissions.
metadata:
  version: "1.3"
  skill-author: Jeremy Leipzig
  last-reviewed: "2026-10-01"
  upstream-version: "TileDB-VCF 0.40.3; tiledb-cloud 0.14.4"
---

# 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](references/verification.md).

## 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`.

For the tested macOS ARM64 environment, pins below avoid versioned-library import
failures in the otherwise successful current Conda solve:

```bash
conda create -n tiledb-vcf -c tiledb -c conda-forge \
  python=3.12 tiledbvcf-py=0.40.3 \
  azure-core-cpp=1.16.2 azure-storage-blobs-cpp=12.16.0 \
  azure-storage-files-datalake-cpp=12.14.0 capnproto=1.4.0 c-blosc2=2.23.1
conda activate tiledb-vcf
python -c 'import tiledbvcf; print(tiledbvcf.version)'
tiledbvcf version
```

The equivalent Micromamba solve/install was executed. These extra ABI pins are a
verified macOS ARM64 workaround, not a claim about all platforms. Preserve the
resolved environment for production. Other-platform installation and official
Docker images are alternatives documented upstream, not tested here.

## Prepare and create a cohort

1. Record reference assembly, contig naming/lengths, callers, normalization rules,
   sample identity, and file checksums. Do not mix assemblies or assume `1` and
````
END THIRD-PARTY FILE fcb05b8d9435dd70

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