onekgpd
Query 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,
npx skills add K-Dense-AI/scientific-agent-skills --skill onekgpd --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
The skill enables per-individual and region-based queries against the 1000 Genomes Project cohort (3,202 whole-genome-sequenced individuals, GRCh38). It can identify which individuals carry variants matching specified criteria in a gene/region, determine homozygous-reference individuals at a position, and list variants carried by a given set of individuals. It also supports finding which individuals carry certain variants, counting them, and computing relatedness between two named individuals. Returned variant records include 1000 Genomes allele frequencies and gnomAD v4.1 exome/genome frequencies, along with AlphaMissense and HGVSp annotations. Queries are executed through the skill’s wrappers and can be filtered by multiple annotation fields (AF bounds, ClinVar, consequence, impact, etc.). It also provides a complementary offline metadata pathway for sample/population information.
How it works
- Use the wrappers: run scripts/onekgpd_api.py for variant/sample/kinship queries and scripts/onekgpd_meta.py for sample/population metadata. Network access is required for variant/sample queries; metadata is offline.
- Before region-based queries, resolve coordinates against an authoritative source to obtain GRCh38 coordinates (Coordinate Provenance).
- For any query, first run a count command (e.g., count-samples or count-variants) to size the result set, then run the corresponding select command if the count is manageable.
- Default queries consider both heterozygous and homozygous carriage; narrow with --het-only or --hom-only when necessary.
- Output is written to a file (via --output) and a concise stdout summary is printed.
- Variant-returning commands (select- variants, select-variants-in-samples) include detailed fields like af, gnomad_exomes_af, gnomad_genomes_af, am_score, amino_acids, and more.
When to use it
- Find variants carried in a region or set of regions across the cohort (count-variants, then select-variants).
- Find variants carried in a region within a named set of individuals (count-variants-in-samples, then select-variants-in-samples).
- Identify which individuals carry variants matching criteria in a region (count-samples, then select-samples).
- Determine which individuals are homozygous-reference at a single position (count-samples-hom-ref, then select-samples-hom-ref).
- Compute relatedness between two named individuals (kinship).
- Retrieve dataset totals (dataset-info).
What it can touch
- Scripts and commands: scripts/onekgpd_api.py, scripts/onekgpd_meta.py.
- Network endpoint: the public 1000 Genomes query endpoint over TLS for variant/sample data.
- Local data file: offline sample/population metadata for metadata queries.
Caveats
- Requires Python >=3.11.
- Variant and sample queries require outbound network access to the public 1000 Genomes query endpoint over TLS; sample/population metadata runs offline from a bundled data file.
- No credentials or API keys are required.
- The user must resolve coordinates to GRCh38 before region queries; otherwise results may target an unintended location.
- The skill is designed specifically for the 1000 Genomes Project dataset and is not intended for other cohorts.
# OneKGPd: Individual-Level Queries over the 1000 Genomes Project ## Scope This skill queries the 1000 Genomes Project dataset — 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 — heterozygous or homozygous — is a criterion that queries may specify; results are returned as variants or as sample names, not as raw genotypes. ## When to Use **Use this skill when you need to:** - Find **variants** carried
- Scope
- When to Use
- Prerequisites
- Core Rules
- Coordinate Provenance (MANDATORY FIRST STEP)
- Command Selection Guide
- Annotation filters (shared across variant and sample selection/counting)
- Quick Start
- Commands
- Variant-returning commands
- Sample-returning commands
- Homozygous-reference commands
- Relatedness command
- Dataset metadata command
Resolve gene symbol -> GRCh38 region with an authoritative source FIRST, then pass the verified coordinates to the OneKGPd query below. Step 1. Resolve coordinates against an authoritative source — see Coordinate Provenance. Step 2. Size the result set: how many individuals carry predicted likely-pathogenic missense variants in this region? uv run scripts/onekgpd_api.py count-samples \ Step 3. If the count is manageable, list those individuals. uv run scripts/onekgpd_api.py select-samples \ Step 4: For that set of individuals, see the actual variants they carry. uv run scripts/onekgpd_api.py select-variants-in-samples \
What does the onekgpd skill do?
Query 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,
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill onekgpd --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.