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 thesecondfox/skill --skill gnomad-databasegit clone --depth 1 https://github.com/thesecondfox/skillWrote 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/thesecondfox/skill/gnomad-database)<a href="https://agentmods.dev/skills/thesecondfox/skill/gnomad-database"><img src="https://agentmods.dev/badge/skills/thesecondfox/skill/gnomad-database.svg" alt="Measured on agentmods" 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 | $0.00058 | $0.03258 |
| Opus 5 | $0.00029 | $0.01629 |
| Sonnet 5 | $0.00012 | $0.00652 |
| Haiku 4.5 | $0.00006 | $0.00326 |
Grade A, and why
gnomad-database scanned grade A with 1 finding 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.post(url, json={"query": query, "variables": variables}) This is a copy
100% identical to gnomad-database — 0 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 — 396 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gnomAD Database
Overview
The Genome Aggregation Database (gnomAD) is the largest publicly available collection of human genetic variation, aggregated from large-scale sequencing projects. gnomAD v4 contains exome sequences from 730,947 individuals and genome sequences from 76,215 individuals across diverse ancestries. It provides population allele frequencies, variant consequence annotations, and gene-level constraint metrics that are essential for interpreting the clinical significance of genetic variants.
Key resources:
- gnomAD browser: https://gnomad.broadinstitute.org/
- GraphQL API: https://gnomad.broadinstitute.org/api
- Data downloads: https://gnomad.broadinstitute.org/downloads
- Documentation: https://gnomad.broadinstitute.org/help
When to Use This Skill
Use gnomAD when:
- Variant frequency lookup: Checking if a variant is rare, common, or absent in the general population
- Pathogenicity assessment: Rare variants (MAF < 1%) are candidates for disease causation; gnomAD helps filter benign common variants
- Loss-of-function intolerance: Using pLI and LOEUF scores to assess whether a gene tolerates protein-truncating variants
- Population-stratified frequencies: Comparing allele frequencies across ancestries (African/African American, Admixed American, Ashkenazi Jewish, East Asian, Finnish, Middle Eastern, Non-Finnish European, South Asian)
- ClinVar/ACMG variant classification: gnomAD frequency data feeds into BA1/BS1 evidence codes for variant classification
- Constraint analysis: Identifying genes depleted of missense or loss-of-function variation (z-scores, pLI, LOEUF)
Core Capabilities
1. gnomAD GraphQL API
gnomAD uses a GraphQL API accessible at https://gnomad.broadinstitute.org/api. Most queries fetch variants by gene or specific genomic position.
Datasets available:
gnomad_r4— gnomAD v4 exomes (recommended default, GRCh38)gnomad_r4_genomes— gnomAD v4 genomes (GRCh38)gnomad_r3— gnomAD v3 genomes (GRCh38)gnomad_r2_1— gnomAD v2 exomes (GRCh37)
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.
- 3d ago First seen · 396 lines · 58 tokens per session scan A e48190e2e316
gnomad-database is a skill published in the GitHub repository thesecondfox/skill (3 stars, last pushed 5mo ago), licensed MIT. It adds 58 tokens to every session and 3,258 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to gnomad-database, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…