Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PKU-YuanGroup/OpenAI4S --skill bio-clinical-databases-gnomad-frequenciesgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-clinical-databases-gnomad-frequencies)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-clinical-databases-gnomad-frequencies"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clinical-databases-gnomad-frequencies/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-clinical-databases-gnomad-frequencies"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clinical-databases-gnomad-frequencies.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00123 | $0.07014 |
| Opus 5 | $0.00062 | $0.03507 |
| Sonnet 5 | $0.00025 | $0.01403 |
| Haiku 4.5 | $0.00012 | $0.00701 |
Grade B, and why
bio-clinical-databases-gnomad-frequencies scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
- Python (single variant): GraphQL via `requests.post('https://gnomad.broadinstitute.org/api', json={'query': ..., 'variables': ...})` Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Python (single variant): GraphQL via `requests.post('https://gnomad.broadinstitute.org/api', json={'query': ..., 'variables': ...})` This is a copy
98% identical to bio-clinical-databases-gnomad-frequencies — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 431 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: requests 2.31+, hail 0.2.130+, pandas 2.2+, myvariant 1.0+. Current gnomAD release is v4.1 (May 2024); v4.1 fixed the v4.0 AN under-counting issue that inflated rare-variant AF estimates by 5-10%.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - Hail:
hl.version(); pin to >=0.2.130 for v4 schema
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. The gnomAD browser GraphQL API at https://gnomad.broadinstitute.org/api is the supported public endpoint; Hail Tables on Google Cloud Storage at gs://gcp-public-data--gnomad/ are the supported bulk access.
gnomAD Frequency Queries and Constraint
'How rare is this variant in the general population?' -> Pull allele frequency, grpmax FAF95 (the ACMG-grade frequency), LOEUF gene-level constraint, structural variant catalog, mtDNA frequencies, and the appropriate dataset version per use case.
- Python (single variant): GraphQL via
requests.post('https://gnomad.broadinstitute.org/api', json={'query': ..., 'variables': ...}) - Python (aggregator):
myvariant.MyVariantInfo().getvariant(hgvs, fields=['gnomad_exome', 'gnomad_genome']) - Python (bulk):
hl.read_table('gs://gcp-public-data--gnomad/release/4.1/ht/exomes/gnomad.exomes.v4.1.sites.ht')
v2.1.1 / v3.1.2 / v4.x: When to Use Which
This is the most consequential decision in any gnomAD query. The releases are not interchangeable; choice determines what can and cannot be said about a variant.
| Release | Build | Samples | Use when | Fails when |
|---|---|---|---|---|
| v2.1.1 | GRCh37 | 125,748 exomes + 15,708 genomes | Constraint metrics needed (LOEUF v2 most-validated); chrX/Y constraint required; GRCh37 native non-negotiable | GRCh38 native cohort; modern rare-variant FAF95 (use v4) |
| v3.1.2 | GRCh38 | 76,156 genomes (NO exomes) | Non-coding region rare variants on GRCh38; mtDNA frequencies | Exome variants needed (no exomes); 76k cohort smaller than v4 |
| v4.0/v4.1 | GRCh38 | 730,947 exomes + 76,215 genomes = 807,162 total | Default for everything; rare-variant filtering, FAF95, gene queries | chrX/Y constraint (not released); cancer-cohort analysis (no TCGA in v4) |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 431 lines · 123 tokens per session scan B d753f1b457b2
bio-clinical-databases-gnomad-frequencies is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 123 tokens to every session and 7,014 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). It is 98% identical to bio-clinical-databases-gnomad-frequencies, differing in 12 lines, and is treated as a copy.
Other skills, from other repositories
boltz-structure-prediction
Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC…
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
flow-cytometry-analysis
Complete flow cytometry analysis pipeline. FCS file handling, compensation, manual/automated gating, immunophenotyping, CFSE proliferation analysis, cell cycle analysis (Dean-Jett-Fox), and apoptosis assays. Extends flowio with analytical workflows. For raw FCS parsing only use flowio.
scientific-critical-thinking
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review…
cellxgene-census
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
glycobiology
Glycosylation site prediction and glycobiology analysis. N-glycosylation motif finding, O-glycosylation hotspot prediction, glycan structure resources. Lightweight, pure Python. For protein function queries use uniprot-database; for structure analysis use alphafold-database.