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 gtex-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/gtex-database)<a href="https://agentmods.dev/skills/thesecondfox/skill/gtex-database"><img src="https://agentmods.dev/badge/skills/thesecondfox/skill/gtex-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 | $0.00061 | $0.02914 |
| Opus 5 | $0.00030 | $0.01457 |
| Sonnet 5 | $0.00012 | $0.00583 |
| Haiku 4.5 | $0.00006 | $0.00291 |
Grade A, and why
gtex-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.get(url, params=params, headers={"Accept": "application/json"}) This is a copy
100% identical to gtex-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 — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GTEx Database
Overview
The Genotype-Tissue Expression (GTEx) project provides a comprehensive resource for studying tissue-specific gene expression and genetic regulation across 54 non-diseased human tissues from nearly 1,000 individuals. GTEx v10 (the latest release) enables researchers to understand how genetic variants regulate gene expression (eQTLs) and splicing (sQTLs) in a tissue-specific manner, which is critical for interpreting GWAS loci and identifying regulatory mechanisms.
Key resources:
- GTEx Portal: https://gtexportal.org/
- GTEx API v2: https://gtexportal.org/api/v2/
- Data downloads: https://gtexportal.org/home/downloads/adult-gtex/
- Documentation: https://gtexportal.org/home/documentationPage
When to Use This Skill
Use GTEx when:
- GWAS locus interpretation: Identifying which gene a non-coding GWAS variant regulates via eQTLs
- Tissue-specific expression: Comparing gene expression levels across 54 human tissues
- eQTL colocalization: Testing if a GWAS signal and an eQTL signal share the same causal variant
- Multi-tissue eQTL analysis: Finding variants that regulate expression in multiple tissues
- Splicing QTLs (sQTLs): Identifying variants that affect splicing ratios
- Tissue specificity analysis: Determining which tissues express a gene of interest
- Gene expression exploration: Retrieving normalized expression levels (TPM) per tissue
Core Capabilities
1. GTEx REST API v2
Base URL: https://gtexportal.org/api/v2/
The API returns JSON and does not require authentication. All endpoints support pagination.
import requests
BASE_URL = "https://gtexportal.org/api/v2"
def gtex_get(endpoint, params=None):
"""Make a GET request to the GTEx API."""
url = f"{BASE_URL}/{endpoint}"
response = requests.get(url, params=params, headers={"Accept": "application/json"})
response.raise_for_status()
return response.json()
2. Gene Expression by Tissue
import requests
import pandas as pd
def get_gene_expression_by_tissue(gene_id_or_symbol, dataset_id="gtex_v10"):
"""Get median gene expression across all tissues."""
url = "https://gtexportal.org/api/v2/expression/medianGeneExpression"
params = {
"gencodeId": gene_id_or_symbol,
"datasetId": dataset_id,
"itemsPerPage": 100
}
response = requests.get(url, params=params)
data = response.json()
records = data.get("data", [])
df = pd.DataFrame(records)
if not df.empty:
df = df[["tissueSiteDetailId", "tissueSiteDetail", "median", "unit"]].sort_values(
"median", ascending=False
)
return df
# Example: get expression of APOE across tissues
df = get_gene_expression_by_tissue("ENSG00000130203.10") # APOE GENCODE ID
# Or use gene symbol (some endpoints accept both)
print(df.head(10))
# Output: tissue name, median TPM, sorted by highest expression
What ships with it
1 file 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 · 316 lines · 61 tokens per session scan A aff87c4f25bb
gtex-database is a skill published in the GitHub repository thesecondfox/skill (3 stars, last pushed 5mo ago), licensed MIT. It adds 61 tokens to every session and 2,914 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 gtex-database, differing in 0 lines, and is treated as a copy.
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