Query ARCHS4 REST API for uniformly processed RNA-seq expression, tissue patterns, co-expression across 1M+ human/mouse samples. Retrieve z-scores, co-expressed genes, samples by metadata, HDF5 matrices. For variant population genetics use gnomad-database; for pathway enrichment use gget-genomic-databases (Enrichr).
SkillScore91Maintained
Automatic
◆Research pack
Last verified 9/3/2026
What it changes
Passed 18 of 20 test cases with the skill, and 10 of 20 without it.
Without skill48%
With skill91%
Checks it passed that the model alone missed
Identifies HDF5 path meta/genes/gene_symbol.
Extracts the ensembl_gene_id key from the response.
Identifies liver as the top expressing tissue for TTR.
and 9 more in the full report
10 fixed11 passed either way2 still failing0 broke
Pass-rate lift
+43 pp
Tokens
+283%
Agent turns
0%
Response time
-27%
Model gemini-3.6-flashMeasured 9/3/2026v221 of 23 cases passed with skill · 20 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 9
Prompt
What is the direct download URL for the ARCHS4 v2.6 human gene-level HDF5 expression matrix file?
case-12 · gemini-3.6-flash · 9/3/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md631 linessingle file
ARCHS4 Database
Overview
ARCHS4 (All RNA-seq and ChIP-seq Sample and Signature Search) is a resource of uniformly aligned and processed human and mouse RNA-seq data from NCBI GEO and SRA, covering 1 million+ samples. The REST API at https://maayanlab.cloud/archs4/api/ provides gene-level expression profiles, z-score normalized tissue expression, co-expression networks, and sample metadata search — all without authentication. Large-scale bulk queries can also use the downloadable HDF5 expression matrices.
When to Use
Retrieving tissue-specific or cell-type-specific expression z-scores for a gene of interest across hundreds of tissue types
Finding genes co-expressed with a query gene (co-expression network construction or guilt-by-association analysis)
Searching for RNA-seq samples by tissue, disease, or metadata keyword to identify candidate datasets for reanalysis
Comparing expression profiles of multiple genes across tissues to prioritize candidates for wet-lab follow-up
Data requirements: gene symbols (HGNC format, e.g., TP53, BRCA1); sample GEO/SRA IDs for direct sample queries
Environment: internet connection; no API key or account required
Rate limits: ~10 requests/second; add time.sleep(0.1) between sequential gene queries to avoid throttling
pip install requests pandas matplotlib seaborn
Quick Start
import requests
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1"
def archs4_get(endpoint: str, params: dict = None) -> dict:
"""Send a GET request to the ARCHS4 API and return parsed JSON."""
r = requests.get(f"{ARCHS4_BASE}/{endpoint}", params=params, timeout=30)
r.raise_for_status()