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Get Started Free →Query the Ensembl database to resolve gene, transcript, and protein IDs, fetch genomic or protein sequences, retrieve gene structures (exons), and get variant consequence and effect predictions (VEP). Use this skill as a primary ID translator, genomic sequence database and variant effect prediction tool.
.claude/skills/mkurman-ensembl-database/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-14 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-13 | ✓→✗ | ▼ Worse | -11% | 0% |
| case-21 | ✓→✗ | ▼ Worse | 46% | 0% |
| case-15 | ✓→✓ | = Same ✓ | 77% | 0% |
| case-20 | ✓→✓ | = Same ✓ | 97% | 0% |
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://useast.ensembl.org/index.html and https://github.com/Ensembl/ensembl-rest/wiki, then (2) create the file recording the notification text and timestamp.
The Ensembl database is a resource for genome annotation. This skill allows you to interact with the Ensembl REST API to resolve ambiguous symbols, cross-reference IDs (RefSeq, HGNC, UniProt, ENSG), fetch raw sequences, and retrieve detailed transcript structures.
Key Concepts:
different three-letter species codes.
isoform).
NCBI.
available or non-human).
database rather than accessing the database directly. The scripts automatically enforce the required rate limit gracefully.
default to "human". You MUST explicitly flag this default to the user to ensure they are aware.
the MANE Select transcript (for human) or the Canonical transcript (for others) unless the user explicitly asks for all alternative isoforms. You MUST flag to the user when multiple transcripts are available and you are defaulting to the primary one.
you MUST use the --assembly GRCh37 flag. You MUST explicitly flag to the user when a non-default assembly is being used.
files in /tmp by default, or to a user-specified file using the --output flag. It also prints a concise summary to stdout.
output.
1. Resolve Gene ID — Resolve a symbol, alias, or RefSeq ID to ENSG ID(s). Automatically falls back to resolving synonyms if primary symbol is not found.
bashuv run scripts/ensembl_api.py resolve-gene TP53 --species human --output tp53.json uv run scripts/ensembl_api.py resolve-gene PCL2 --output pcl2.json # Falls back to synonym resolution
2. Map ID to External Database — Cross-reference an Ensembl ID to UniProt, HGNC, RefSeq, etc.
bashuv run scripts/ensembl_api.py map-id ENSG00000141510 --external-db UniProt --output uniprot_map.json uv run scripts/ensembl_api.py map-id ENST00000269305 --external-db RefSeq_mRNA --output refseq_map.json
3. Get Genomic Sequence — Fetch raw DNA for a coordinate window. Supports GRCh37 via --assembly GRCh37.
bashuv run scripts/ensembl_api.py get-sequence 17:7661779-7687550 --species human --output seq.txt uv run scripts/ensembl_api.py get-sequence chr9:21971100-21971200 --assembly GRCh37 --output seq_grch37.txt
4. Gene Summary — High-level metadata: symbol, biotype, description, chromosomal location.
bashuv run scripts/ensembl_api.py gene-summary ENSG00000141510 --output gene_summary.json
5. List Transcripts — All transcripts for a gene, with optional --only-mane or --only-canonical filters. Output includes Transcript Support Level (TSL).
bashuv run scripts/ensembl_api.py transcripts ENSG00000141510 --only-mane --output transcripts_mane.json uv run scripts/ensembl_api.py transcripts ENSG00000141510 --only-canonical --output transcripts_canonical.json uv run scripts/ensembl_api.py transcripts ENSG00000141510 --output transcripts_all.json
5b. Canonical TSS — Get the single coordinate of the Transcription Start Site (TSS) for the canonical transcript of a gene.
> !NOTE] Unlike the standard transcripts command, canonical-tss accepts > both symbols (e.g., TP53) and Ensembl IDs, and automatically resolves them. > It also does the math for strand orientation (TSS is Start for + strand > and End for - strand), outputting the single integer coordinate directly.
bashuv run scripts/ensembl_api.py canonical-tss TP53 --output tp53_tss.json uv run scripts/ensembl_api.py canonical-tss ENSG00000141510 --output tss.json
6. Transcript Structure — Exon coordinates, CDS boundaries, and computed 5'/3' UTR regions for a transcript.
bashuv run scripts/ensembl_api.py transcript-structure ENST00000269305 --output structure.json
7. Protein Info — ENSP ID and sequence length for a transcript.
bashuv run scripts/ensembl_api.py protein-info ENST00000269305 --output protein_info.json
8. Protein Sequence — Amino acid FASTA for a transcript (ENST) or protein (ENSP) ID.
bashuv run scripts/ensembl_api.py protein-sequence ENST00000269305 --output protein.fasta uv run scripts/ensembl_api.py protein-sequence ENSP00000269305 --output protein_ensp.fasta
9. Variant Consequence (VEP) — Predict molecular consequences for a genomic variant. Includes open-licensed plugins: AlphaMissense, Conservation, DosageSensitivity, IntAct, MaveDB, OpenTargets, LoF (Loftee), NMD, UTRAnnotator, mutfunc, LOEUF.
bashuv run scripts/ensembl_api.py vep 9:21971147:T:C --species human --output vep.json uv run scripts/ensembl_api.py vep rs699 --species human --output vep_rs699.json
Example VEP stdout output:
[*] Variant: 9:21971147:T>C
[*] Most severe consequence: missense_variant
[*] Found 15 transcript consequences.
[*] VEP Predictions:
- ENST00000304494 (CDKN2A): Consequence = missense_variant
- ENST00000304494 (CDKN2A): Amino Acids = N/S
- ENST00000304494 (CDKN2A): SIFT = deleterious (0.01)
- ENST00000304494 (CDKN2A): AlphaMissense Class = likely_benign
- ENST00000304494 (CDKN2A): AlphaMissense Pathogenicity = 0.2129
- ENST00000304494 (CDKN2A): Conservation = 2.05
- ENST00000304494 (CDKN2A): Dosage Sensitivity (Haplo) = 0.889228328567991
- ENST00000304494 (CDKN2A): Dosage Sensitivity (Triplo) = 0.135514349094646
- ENST00000304494 (CDKN2A): Loss of Function (LOEUF) = 0.791Presenting VEP Results: After running the VEP command, you MUST present the full VEP Predictions list from stdout to the user. This list contains both standard VEP predictions (Consequence, Amino Acids, SIFT, PolyPhen) and open-license plugin results (AlphaMissense, Conservation, Dosage Sensitivity, LOEUF, Loftee LoF, NMD, UTRAnnotator, Mutfunc). Do NOT just summarize — show the complete list so the user can see all predictions. If the list is very long (many transcripts), show the MANE Select / canonical transcript rows in full and note that the complete data is in the JSON output.
If the user needs detailed, nested structural data (like the precise integer coordinates of Exon 2 of a transcript) that isn't summarized in stdout:
--output or the temporary filepath printed by the script).
jq or write a quick, disposable python snippet toextract the specific data point requested. Do not attempt to read the entire JSON file into your context if it is very large.
If you need to make an API call that the script does not support (e.g., fetching protein domain annotations, coordinate mapping between assemblies, homology searches, linkage disequilibrium, or phenotype lookups), read references/ensembl_rest_api_reference.md for a complete reference of available endpoints, parameters, and response fields.
CRITICAL: When writing custom scripts or using alternatives to the provided scripts, you MUST respect the Ensembl REST API rate limits (maximum 15 requests per second) and handle 429 Too Many Requests errors gracefully (e.g., with exponential backoff).
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | fail→fail | 10,652 | 8,237 | -23% | 1 | 1 | 0% | 2,032 | 2,839 | +40% | 0 | 0 | — |
case-01 | fail→fail | 24,677 | 8,055 | -67% | 1 | 1 | 0% | 5,205 | 3,012 | -42% | 0 | 0 | — |
case-02 | fail→fail | 25,722 | 8,255 | -68% | 1 | 1 | 0% | 4,072 | 2,860 | -30% | 0 | 0 | — |
case-04 | fail→fail | 13,362 | 6,075 | -55% | 1 | 1 | 0% | 1,688 | 2,746 | +63% | 0 | 0 | — |
case-05 | fail→fail | 4,956 | 6,983 | +41% | 1 | 1 | 0% | 907 | 2,781 | +207% | 0 | 0 | — |
case-06 | fail→fail | 9,627 | 8,091 | -16% | 1 | 1 | 0% | 1,728 | 3,044 | +76% | 0 | 0 | — |
case-07 | fail→fail | 11,293 | 7,525 | -33% | 1 | 1 | 0% | 1,474 | 2,939 | +99% | 0 | 0 | — |
case-08 | fail→fail | 18,760 | 9,784 | -48% | 1 | 1 | 0% | 2,744 | 3,075 | +12% | 0 | 0 | — |
case-09 | fail→fail | 10,659 | 7,784 | -27% | 1 | 1 | 0% | 1,958 | 2,793 | +43% | 0 | 0 | — |
case-10 | fail→fail | 4,063 | 7,940 | +95% | 1 | 1 | 0% | 657 | 2,687 | +309% | 0 | 0 | — |
case-11 | fail→fail | 24,853 | 6,263 | -75% | 1 | 1 | 0% | 4,683 | 2,836 | -39% | 0 | 0 | — |
case-12 | fail→fail | 5,389 | 6,229 | +16% | 1 | 1 | 0% | 1,194 | 2,746 | +130% | 0 | 0 | — |
case-17 | fail→fail | 11,220 | 7,914 | -29% | 1 | 1 | 0% | 553 | 2,734 | +394% | 0 | 0 | — |
case-13 | pass→fail | 17,193 | 7,564 | -56% | 1 | 1 | 0% | 3,376 | 2,993 | -11% | 0 | 0 | — |
case-14 | fail→pass | 16,334 | 4,512 | -72% | 1 | 1 | 0% | 2,747 | 3,168 | +15% | 0 | 0 | — |
case-15 | pass→pass | 10,823 | 7,253 | -33% | 1 | 1 | 0% | 2,027 | 3,579 | +77% | 0 | 0 | — |
case-16 | fail→fail | 7,721 | 6,154 | -20% | 1 | 1 | 0% | 1,373 | 2,679 | +95% | 0 | 0 | — |
case-18 | fail→fail | 6,050 | 6,915 | +14% | 1 | 1 | 0% | 899 | 2,964 | +230% | 0 | 0 | — |
case-19 | fail→fail | 7,866 | 10,447 | +33% | 1 | 1 | 0% | 1,617 | 2,859 | +77% | 0 | 0 | — |
case-20 | pass→pass | 13,270 | 10,960 | -17% | 1 | 1 | 0% | 2,267 | 4,460 | +97% | 0 | 0 | — |
case-21 | pass→fail | 16,138 | 7,588 | -53% | 1 | 1 | 0% | 1,891 | 2,759 | +46% | 0 | 0 | — |
case-22 | pass→pass | 15,134 | 14,263 | -6% | 1 | 1 | 0% | 2,666 | 5,032 | +89% | 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 4 counted toward the lift figure. The other 18 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 -5 percentage points is the difference between those two pass rates over the 4 comparable cases. 4 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.