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Get Started Free →Use when you want to retrieve quantitative RNA expression data and variant eQTL information from the GTEx (Genotype-Tissue Expression) Project across 54 non-diseased tissue sites.
.claude/skills/mkurman-gtex-database/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-14 | ✗→✓ | ▲ Improved | 58% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 59% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 62% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 83% | 0% |
| case-04 | ✓→✗ | ▼ Worse | 64% | 0% |
This skill retrieves transcriptomics data (RNA expression baselines) and expression Quantitative Trait Loci (eQTLs) from the GTEx Portal API V2. It provides access to median TPM (Transcripts Per Million) values for genes and significant eQTLs for variants across 54 human tissue sites.
uv: Read the uv skill and follow its Setup instructions to ensureuv is installed and on PATH.
this skill directory then (1) prominently notify the user to check the terms at https://gtexportal.org/home/license and https://gtexportal.org/home/documentationPage#gtexApi, then (2) create the file recording the notification text and timestamp.
Use this skill when you need to:
various tissues.
window.
Do NOT use when you need to:
(PTMs). GTEx only measures mRNA abundance.
GTEx is a baseline atlas of normal, non-diseased tissues.
CRITICAL: You MUST respect GTEx Portal API Terms of Use.
database rather than accessing the database directly. The scripts automatically enforce the required rate limit gracefully.
output.
Pick the right command on the first try. Match the user's input to the correct subcommand below.
resolve-gencode-idget-median-expressionget-top-expressed-tissuesget-gene-eqtlsget-eqtls-in-regionbash# Map the TNF gene symbol to its GENCODE ID uv run scripts/gtex_cli.py resolve-gencode-id TNF --output /tmp/tnf_id.json # Get median expression of a gene by GENCODE ID uv run scripts/gtex_cli.py get-median-expression ENSG00000232810.2 --output /tmp/tnf_expr.json
All subcommands write JSON to disk. Always save output in the /tmp/ directory. The default output file is /tmp/gtex_output.json if --output is not specified.
resolve-gencode-id — Gene Symbol → GENCODE IDMaps a standard gene symbol (e.g., "JUN", "TNF") to its Versioned GENCODE ID. This ID is required for all other expression and eQTL calls.
bashuv run scripts/gtex_cli.py resolve-gencode-id TNF --output /tmp/tnf_id.json
Arguments:
gene_symbol (positional): The standard gene symbol (e.g., "TNF").--output: Output file path (default: /tmp/gtex_output.json).get-median-expression — Get Median Expression (TPM)Retrieves the median TPM for a gene across all 54 GTEx tissue sites or specified tissues.
bashuv run scripts/gtex_cli.py get-median-expression ENSG00000232810.2 \ --tissues "Whole Blood,Spleen" --output /tmp/expr.json
Arguments:
gencode_id (positional): The Versioned GENCODE ID.--tissues: Comma-separated list of tissue IDs (optional, defaults to all54 tissues).
--output: Output file path (default: /tmp/gtex_output.json).get-top-expressed-tissues — Get Top Expressed TissuesReturns the n tissues with the highest median expression for the target gene.
bashuv run scripts/gtex_cli.py get-top-expressed-tissues ENSG00000232810.2 \ --n 5 --output /tmp/top_tissues.json
Arguments:
gencode_id (positional): The Versioned GENCODE ID.--n: Number of top tissues to return (default: 5).--output: Output file path.get-gene-eqtls — Get All eQTLs for a GeneReturns every significant eQTL associated with the gene across specified tissues.
bashuv run scripts/gtex_cli.py get-gene-eqtls ENSG00000232810.2 \ --tissues "Whole Blood" --output /tmp/eqtls.json
Arguments:
gencode_id (positional): The Versioned GENCODE ID.--tissues: Comma-separated list of tissue IDs (optional, defaults to all).--output: Output file path.get-eqtls-in-region — Get eQTLs in Chromosomal RegionReturns all significant single-tissue eQTLs within a chromosomal window (up to 8Mb).
bashuv run scripts/gtex_cli.py get-eqtls-in-region chr17 7000000 7100000 "Esophagus - Muscularis" \ --output /tmp/region_eqtls.json
Arguments:
chromosome (positional): Chromosome name (e.g., chr17).start (positional): Start position.end (positional): End position (max 8Mb from start).tissue_id (positional): The target tissue ID.--output: Output file path.bash# Step 1: Map symbol to GENCODE ID uv run scripts/gtex_cli.py resolve-gencode-id GATA4 --output /tmp/gata4_id.json # Step 2: Query for top tissues using the resolved ID uv run scripts/gtex_cli.py get-top-expressed-tissues <gencode_id> --n 5 \ --output /tmp/gata4_top.json
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | pass→pass | 10,001 | 8,521 | -15% | 1 | 1 | 0% | 1,502 | 3,190 | +112% | 0 | 0 | — |
case-01 | fail→fail | 9,139 | 7,186 | -21% | 1 | 1 | 0% | 1,468 | 1,984 | +35% | 0 | 0 | — |
case-02 | fail→fail | 11,985 | 7,665 | -36% | 1 | 1 | 0% | 2,007 | 1,944 | -3% | 0 | 0 | — |
case-03 | fail→fail | 12,135 | 8,462 | -30% | 1 | 1 | 0% | 2,098 | 2,165 | +3% | 0 | 0 | — |
case-04 | pass→fail | 16,547 | 43,842 | +165% | 1 | 1 | 0% | 1,383 | 2,267 | +64% | 0 | 0 | — |
case-06 | fail→fail | 10,788 | 8,826 | -18% | 1 | 1 | 0% | 1,609 | 2,222 | +38% | 0 | 0 | — |
case-07 | pass→pass | 10,985 | 7,727 | -30% | 1 | 1 | 0% | 1,654 | 2,314 | +40% | 0 | 0 | — |
case-08 | fail→fail | 12,914 | 3,329 | -74% | 1 | 1 | 0% | 2,031 | 2,322 | +14% | 0 | 0 | — |
case-09 | fail→fail | 15,926 | 8,151 | -49% | 1 | 1 | 0% | 2,546 | 2,289 | -10% | 0 | 0 | — |
case-10 | fail→fail | 9,722 | 9,553 | -2% | 1 | 1 | 0% | 1,813 | 2,277 | +26% | 0 | 0 | — |
case-11 | fail→fail | 7,619 | 10,232 | +34% | 1 | 1 | 0% | 1,369 | 2,266 | +66% | 0 | 0 | — |
case-12 | fail→fail | 10,284 | 7,398 | -28% | 1 | 1 | 0% | 1,828 | 2,268 | +24% | 0 | 0 | — |
case-13 | fail→fail | 24,021 | 8,376 | -65% | 1 | 1 | 0% | 2,267 | 2,318 | +2% | 0 | 0 | — |
case-14 | fail→pass | 15,085 | 2,018 | -87% | 1 | 1 | 0% | 1,252 | 1,978 | +58% | 0 | 0 | — |
case-15 | fail→fail | 9,308 | 15,374 | +65% | 1 | 1 | 0% | 1,506 | 3,993 | +165% | 0 | 0 | — |
case-16 | fail→fail | 14,388 | 4,245 | -70% | 1 | 1 | 0% | 2,164 | 2,038 | -6% | 0 | 0 | — |
case-17 | pass→fail | 12,660 | 6,660 | -47% | 1 | 1 | 0% | 2,461 | 1,974 | -20% | 0 | 0 | — |
case-18 | pass→pass | 7,487 | 6,917 | -8% | 1 | 1 | 0% | 1,174 | 2,838 | +142% | 0 | 0 | — |
case-19 | fail→pass | 15,124 | 10,796 | -29% | 1 | 1 | 0% | 2,221 | 3,525 | +59% | 0 | 0 | — |
case-20 | fail→pass | 7,501 | 1,562 | -79% | 1 | 1 | 0% | 1,174 | 1,901 | +62% | 0 | 0 | — |
case-21 | fail→pass | 8,942 | 5,229 | -42% | 1 | 1 | 0% | 1,420 | 2,597 | +83% | 0 | 0 | — |
case-22 | pass→pass | 13,587 | 3,291 | -76% | 1 | 1 | 0% | 2,459 | 2,362 | -4% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 10 counted toward the lift figure. The other 12 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +9 percentage points is the difference between those two pass rates over the 10 comparable cases. 5 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.