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OneKGPd — Individual-Level 1000 Genomes Queries

Query the 1000 Genomes Project cohort (3,202 genomes, GRCh38) at the level of individual participants and variants.

Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 8/10Last updated: Aug 9, 2026

What it does

Gives Claude structured access to the extended high-coverage 1000 Genomes Project cohort (3,202 whole-genome-sequenced individuals, GRCh38) at individual resolution via two bundled helper scripts:

  • Select variants in one or more regions, cohort-wide or restricted to a named sample set
  • Select or count which individuals carry variants matching your criteria
  • List individuals who are homozygous reference (0/0) at a specific position
  • Compute relatedness between two named participants (degree + KING kinship coefficient)
  • Look up population, sex, and pedigree metadata fully offline

Filters include 1000 Genomes AF, gnomAD v4.1 exome/genome AF, AlphaMissense score and class, ClinVar significance, and VEP impact/biotype/consequence, plus het-only or hom-only zygosity narrowing.

Who it's for

  • Population-genomics and rare-variant researchers or graduate students
  • Bioinformatics engineers who need carrier sets from the cohort quickly
  • Anyone using 1000 Genomes as a control cohort in an analysis pipeline

Examples

  1. Find carriers of predicted-pathogenic missense variants in BRCA1: verify chr17:43044292-43170245 in Ensembl, run count-samples to size the set, then select-samples.
  2. Population-scoped variant scan: pull IDs with select-samples-by-population --population YRI, then feed them into select-variants-in-samples.
  3. Check relatedness: kinship --sample1 NA19240 --sample2 NA19239 returns the relationship degree and kinship coefficient.

· · · Install guide · · ·

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/onekgpd folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/onekgpd/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills && mkdir -p ~/.claude/skills && cp -r /tmp/scientific-agent-skills/skills/onekgpd ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Install uv (the scripts run via uv run): curl -LsSf https://astral.sh/uv/install.sh | sh, then confirm with uv --version. Python 3.11+ is required.
  2. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Copy the skill into place: mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/onekgpd ~/.claude/skills/
  4. Restart Claude Code and confirm onekgpd appears in your skill list.
  5. Smoke test: ask Claude for "1000 Genomes dataset info" — the dataset-info command should report 3,202 samples on GRCh38, which also verifies network access.
  6. Important: always resolve gene symbols to GRCh38 coordinates with an authoritative source first. A GRCh37 or mismatched coordinate silently returns results for the wrong locus. No API keys or credentials are needed, but outbound TLS access to the public query endpoint is.
View source on GitHub ↗License: MIT