Skip to content
Data
Skill

/onekgpd

Queries the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific

From plugin
scientific-agent-skills
56k177 skills

Claude Code

$ npx -y skills add K-Dense-AI/scientific-agent-skills --skill onekgpd --agent claude-code

SKILL.md

onekgpd.SKILL.md
name: onekgpd
description: >
  Queries the 1000 Genomes Project dataset (3,202 whole-genome-sequenced
  individuals, GRCh38) at the level of individual participants.
  Use when a question is about individuals or variants in the 1000 Genomes
  Project cohort: which individuals carry variants matching specific criteria
  in a gene or region, which individuals are homozygous-reference at a position,
  which variants exist in the dataset or carried by specified individuals
  in a gene or region, the relatedness between two specified individuals.
  Variants are returned with 1000 Genomes allele frequencies (AF),
  gnomAD v4.1 exome and genome AF, AlphaMissense score, and HGVSp annotations.
license: MIT
compatibility: Requires Python >=3.11. Variant and sample queries require outbound network access to the public 1000 Genomes query endpoint over TLS; the sample/population metadata commands run fully offline over a data file bundled in the skill. No credentials, API keys, or environment variables are used.
allowed-tools: Write Bash
metadata:
  version: "1.4"
  last-reviewed: "2026-09-30"
  upstream-version: "dnaerys 0.2.1; proto R1.20.0"
  skill-author: Dnaerys

OneKGPd: Individual-Level Queries over the 1000 Genomes Project

Scope

This skill queries the 1000 Genomes Project dataset โ€” the extended high-coverage cohort of 3,202 whole-genome-sequenced individuals, on the GRCh38 assembly. All results are drawn from this cohort, and sample names returned by the skill (for example `HG00096` or `NA21130`) identify its participants.

Queries resolve against the cohort's per-individual genotype data. This supports two complementary classes of question: selecting **variants** carried within a region (across the whole cohort or within a specified set of individuals), and selecting the **individuals** who carry variants matching given criteria. Variant selection can be filtered by allele frequency, predicted consequence, clinical significance, AlphaMissense classification, and the other annotation axes listed below. Relatedness between two named individuals is also available.

The genotype state in which a variant is carried โ€” heterozygous or homozygous โ€” is a criterion that queries may specify; results are returned as variants or as sample names, not as raw genotypes.

The public service is **TLS gRPC at `db.dnaerys.org:443`**, accessed with `dnaerys 0.2.1` (Python 3.11+); it is not a REST base URL. The maintained service snapshot advertises VEP 115 / GENCODE 49, ClinVar 202502, and gnomAD 4.1. These are the service's annotation releases, not the latest release of each upstream resource. Record them when interpreting results.

When to Use

**Use this skill when you need to:**

  • Find **variants** carried in a region or set of regions matching some criteria

across the whole cohort (`select-variants`).

  • Find **variants** carried in a region or set of regions matching some criteria

in specific set of individuals (`select-variants-in-samples`).

  • Find **which 1000 Genomes individuals** carry variants matching some criteria

in a region or set of regions (`select-samples`).

  • Count how many individuals carry specific variants (`count-samples`).
  • Restrict any variant query to **heterozygous-only or homozygous-only**

carriage, or query both together (default).

  • Identify which individuals are **homozygous reference** at a single position

(`select-samples-hom-ref`).

  • Determine the **relatedness** between two named 1000 Genomes individuals โ€”

both the degree (twin / 1st / 2nd / 3rd / unrelated) and the KING kinship coefficient (`kinship`).

  • Get **dataset totals** โ€” sample count, sex split, variant count, assembly

(`dataset-info`).

  • Variant selection can be specified by KGP allele frequency, gnomAD 4.1 exome and

gnomAD 4.1 genome allele frequency, AlphaMissense Score and AlphaMissense Class, ClinVar significance (202502), and VEP annotations (impact, biotype, feature type, variant class, consequences).

**Do NOT use this skill for:**

  • Resolving a gene symbol, rsID, or transcript to coordinates, or fetching

reference sequence. Resolve coordinates first (see Coordinate Provenance below), then query this skill with the resolved GRCh38 region.

  • Any cohort other than the 1000 Genomes Project โ€” this skill serves only that

dataset.

Prerequisites

1. **`uv`**: This skill's script is run with `uv run`, which reads the script's inline dependency metadata and provisions an ephemeral environment. Ensure `uv` is installed and on PATH (https://docs.astral.sh/uv/). 2. **Data use terms**: The 1000 Genomes Project data is open; users should be aware of the 1000 Genomes Project / IGSR data-use terms (https://www.internationalgenome.org/data). 3. **Access constraints**: There is no API key, no `.env` file, and no rate-limit token to configure. 4. **Timeouts**: network commands use `--timeout 30` seconds per RPC by default. A positive finite override is allowed. Pagination makes several RPCs and retryable failures retry the whole fetch up to three times, so this is not a deadline for the whole command.

Core Rules

  • **Use the Wrappers**: ALWAYS execute the provided helper scripts rather than

constructing your own client calls or network requests. Use `scripts/onekgpd_api.py` for variant/sample/kinship queries (it handles the connection, streaming, pagination, and JSON serialization), and `scripts/onekgpd_meta.py` for sample/population metadata (offline, see [Sample & population metadata](#sample--population-metadata-offline)).

  • **Coordinates MUST be resolved against an authoritative source first** โ€” see

[Coordinate Provenance](#coordinate-provenance-mandatory-first-step). This is mandatory, not advisory.

  • **Count before you select**: every variant and sample selection has a paired

counting command. Call the count command FIRST to size the result set, t

Read more
@k-dense-ai
Ships withscientific-agent-skills

๐Ÿ”” Claude Scientific Skills is now Scientific Agent Skills. Same skills, broader compatibility โ€” now works with any AI agent that supports the open Agent Skills standard, not just Claude.

Stars on GitHub

Momentum

stars in total
55.6k
Star history of K-Dense-AI/scientific-agent-skills on GitHub, a chart by star-history.com

Other skills on scientific-agent-skills.

aeon

aeon

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting,โ€ฆ

More skills from other plugins.

paddleocr-doc-parsing

paddleocr-doc-parsing

Use this skill to extract structured Markdown/JSON from PDFs and document imagesโ€”tables with cell-level precision, formulas asโ€ฆ

@paddlepaddle@paddlepaddleInstall Skill
context-loader
Auto-invoked

context-loader

Search memories from earlier Pi sessions in this repository. Use it when earlier work may already explain the code, error,โ€ฆ

mem04d69k
@mem0ai@mem0aiInstall Skill
analyzing-ai-citations

analyzing-ai-citations

Answer AI citation and answer engine optimization (AEO) questions in PostHog: whether AI answer engines (ChatGPT, Claude, and anโ€ฆ

posthog10d41k
@posthog@posthogInstall Skill
qmd

qmd

Search local markdown knowledge bases, notes, docs, and wikis with QMD. Use when users ask to find notes, retrieve documents,โ€ฆ

qmd3w31k
@tobi@tobiInstall Skill

Questions about onekgpd.

Which coding agents can use onekgpd?
onekgpd installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.