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Predicts regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence's hosted transformer DNA language models — no local GPU or model weights. Six tasks o
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add K-Dense-AI/scientific-agent-skills --skill genomic-intelligence -a cursor -y
Predicts regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence's hosted transformer DNA language models — no local GPU or model weights. Six tasks over a REST API and a hosted MCP server (keyless public demo): promoter regions, splice donor/acceptor sites, enhancer activity, chromatin state, sequence-to-expression (log TPM), and de-novo gene annotation, plus a composite find-genes-then-predict-expression workflow. Use when the user has a gene symbol, a genomic region, or a DNA/FASTA sequence and wants any of these predictions, mentions Genomic Intelligence, genomicintelligence.ai, api.genomicintelligence.ai, or mcp.genomicintelligence.ai.
requests library for the REST examples. Network access required. The REST /v1 API needs a GI_API_KEY (a gi_ bearer); the hosted MCP server at mcp.genomicintelligence.ai/mcp works keyless against a rate- and concurrency-limited public demo tier, key optional.
metadata:
version: "1.4"
last-reviewed: "2026-10-01"
skill-author: Genomic Intelligence
trigger-keywords: DNA sequence prediction, regulatory genomics, promoter prediction, splice site prediction, enhancer activity, chromatin state, gene expression prediction, sequence to expression, log TPM, gene annotation, transcript prediction, DNA language model, genomic intelligence, hosted inference, Ensembl sequence, FASTA prediction, cis-regulatory, TSS window, DeepSEA, DeepSTARR, BigBird splice, MCP genomics
openclaw:
primaryEnv: GI_API_KEY
envVars:
- name: GI_API_KEY
required: false
description: Optional gi_ bearer key for the REST /v1 API and higher MCP rate and concurrency limits. The hosted MCP demo runs keyless; request a key at .Genomic Intelligence (GI) serves transformer DNA language models over six sequence-analysis tasks on managed GPUs. Give it a gene symbol, a genomic region, or a DNA/FASTA sequence; it returns structured predictions — promoter regions, splice sites, enhancer activity, chromatin state, expression (log TPM), and de-novo gene annotation. Nothing runs locally: no model weights, no GPU, no heavy Python stack. It is a thin client over a hosted, versioned inference API.
Official docs: docs.genomicintelligence.ai ·
REST contract at api.genomicintelligence.ai/v1/openapi.json ·
hosted MCP server at https://mcp.genomicintelligence.ai/mcp
Use GI when the user has DNA and wants a model prediction:
promoter)splice)enhancer)chromatin)expression)annotation)Not for local alignment, variant calling, or file I/O — use a local tool (BioPython, bcftools) for those. GI is for model inference from sequence.
Research and development use. Not for clinical or diagnostic decisions.
GI hosts an MCP server at https://mcp.genomicintelligence.ai/mcp (Streamable
HTTP). When your agent host supports MCP, prefer it: it works keyless against
a rate- and concurrency-limited public demo tier, and an optional gi_ bearer
key raises those limits. It exposes acquisition tools that return a sequence handle
(sequence_ref) and predict_* tools that take that handle, so large sequences
stay out of the context. See MCP workflow below and
references/mcp.md.
Plain HTTP with requests against https://api.genomicintelligence.ai/v1. The
REST path requires a GI_API_KEY (a gi_ bearer). Use it on any host, in
scripts, or when you need the raw envelope. See Core REST workflow.
Authorization: Bearer <key>.
Public GET /v1/tasks/{task}/models discovery needs no key and is rate-limited
by source IP; inspect model windows and bounds before requesting access.
See the current authentication contract.
Request a prediction key at [email protected].GI_API_KEY environment variable
(or a .env via python-dotenv). Never commit keys.export GI_API_KEY="gi_yourkeyhere" # optional for MCP; required for REST
export GI_BASE_URL="https://api.genomicintelligence.ai" # override for staging
Keys are scoped to a partner tier with concurrency and per-minute caps. A 429
means you hit a cap — back off and retry, or ask GI to raise your tier.
Each task is its own published operation with its own request schema, its own
minimum length, and its own closed options object — POST /v1/tasks/promoter/predict, /v1/tasks/splice/predict,
/v1/tasks/enhancer/predict, /v1/tasks/chromatin/predict,
/v1/tasks/annotation/predict, /v1/tasks/expression/predict. Each path is a
literal string, so nothing needs to be constructed, and there is no shared
PredictRequest schema. Body is {sequence, sequence_name?, model?, options?}, returning a {data, meta} envelope. What differs per task:
| Task | Recommended mode | Accepted length | context_window_bp | Notes |
|---|---|---|---|---|
promoter | sync | 300–500,000 bp | 2,000 bp | sliding-window promoter regions |
splice | sync | 100–500,000 bp | 15,000 bp | donor/acceptor sites (long-context BigBird); strand-specific — feed transcript orientation |
enhancer | sync | 50–500,000 bp | 249 bp | dev + housekeeping scores (DeepSTARR, Drosophila) |
chromatin | sync | 200–500,000 bp | 1,000 bp | hundreds of tracks (DeepSEA) |
expression | sync | 9,198–500,000 bp | n/a (trained_window_bp 9,198) | log(TPM+1); needs tss_index unless exactly 9,198 bp, plus a cell-type description |
annotation | async | 1,000–500,000 bp | n/a | de-novo transcripts; submit + poll; sync JSON above 200,000 bp is 413 sync_too_large |
Recommended mode is guidance, not a constraint — every task accepts both. Omit Prefer for a synchronous 200; send Prefer: respond-async for a 202 plus GET /v1/tasks/jobs/{job_id}. The one enforced limit is per operation: where /v1/openapi.json publishes x-sync-limit-bp on a POST, a synchronous JSON request above that length is 413 sync_too_large — 200,000 bp on annotation and 50,000 bp on the composite workflow in contract revision 16. Read the field rather than memorising the numbers. Annotation BED/GFF3 stays synchronous at any admitted length and can time out; the other five predict tasks have no hard sync cap.
The minimum is admission control, not regime. A request above the floor but
shorter than the selected model's bio_spec.context_window_bp is accepted and
scored — against a window padded out to the context window. Enhancer is the
sharp case: the floor is 50 bp but the context window is 249 bp, so 50–248 bp is
scored mostly on padding. Compare your length against
context_window_bp from GET /v1/tasks/{task}/models to know whether the model
saw real sequence. Longer-than-context input is fine — the scanner steps a
prediction window at a time and pads only the final partial window.
Under the floor and over the 500,000 bp cap are both 422 validation_failed
at loc ["body","sequence"]; over-length is not a 413. All lengths are
measured after whitespace is stripped, so a line-wrapped FASTA body can be pasted
verbatim (a > header line still fails the alphabet check).
options is typed and closed (additionalProperties: false) per task — an
unknown key is a hard 422 validation_failed with type: "extra_forbidden",
never ignored:
| Task | options keys |
|---|---|
| promoter | threshold (0–1, default 0.5) |
| splice | threshold (0–1, default 0.5), site_types (subset of ["donor","acceptor"], default both) |
| enhancer | (none) |
| chromatin | threshold (0–1, default 0.5) |
| annotation | batch_size (1–128, default 8), shift_coordinates, reverse_complement (default true) |
| expression | description — required, and the only key |
Prefer: respond-async is a declared header on all six predict operations
and on the composite, not just annotation — see Async.
Omit model and the API uses the task's default — that is the recommended
call. Default model IDs are intentionally not documented here: defaults
change and retired IDs fail hard, so never hardcode one. To pin a model, or to
pick a non-human one (Drosophila, yeast, and Arabidopsis models exist for several
tasks), discover IDs at call time with GET /v1/tasks/{task}/models (REST) or
list_models (MCP) — and never invent one. Full per-task output shapes are
in references/tasks.md.
expression is the strictest of the six: alone among them its schema requires
options as well as sequence. Three hard rules it enforces — every violation
is a 422, nothing is padded or clamped, and there is no opt-out flag, header,
or query parameter:
sequence[tss_index-4599 : tss_index+4599]. The endpoint itself accepts
9,198–500,000 bp; anything below 9,198 bp is rejected outright.tss_index is required unless the sequence is exactly 9,198 bp. It is the
0-based TSS offset into the whitespace-stripped sequence, bounded by
4599 ≤ tss_index ≤ len(sequence) − 4599. At exactly 9,198 bp it defaults to
4,599, the only legal value there. So you may submit a whole locus (up to
500 kb) and let the server cut the window — but the server does not
discover the TSS for you (that is the composite workflow's job), and does
not reverse-complement: submit gene-sense sequence.options.description — a cell-type / assay string (e.g. "K562 cells") —
is required, and is the only key expression accepts inside options.
Unknown top-level body fields are rejected too.Note: the legal
tss_indexrange is wide, so an offset that is merely wrong (counted over raw FASTA characters including newlines, or relative to a locus start rather than the submitted slice) does not error — it returns a confident200for the wrong window. Assert onmeta.task_specific_counts.scored_window/.tss_indexin the response. The submitted length ismeta.sequence_length; the scored width is always 9,198, i.e.scored_window[1] - scored_window[0]. In revision 16,data.inputcontains onlysequence_name,description, andtss_index; it does not contain the submitted length or scored window.Both
tss_indexviolations — "required unless exactly 9,198 bp" and the range check — come from a whole-model validator, so they surface at the body level rather than undertss_index. Match onerror.code == "validation_failed"and use the message for display only. Anyloctuple quoted in this skill is illustrative of that shape, not part of the contract: it is not published in the schema and must not be branched on.
You rarely start from a raw 9,198 bp string. Acquire sequence first:
fetch_ensembl_sequence(gene=...); from
coordinates → fetch_region(region=...). Both acquire public reference sequence (no key), using a bundled coordinate
catalog, cache, UCSC, or Ensembl; retain the returned provenance. REST users can query Ensembl REST directly. (find_genes is
the annotation task, not an acquisition tool.)expression → use the TSS-centred fetch so the window is exactly
9,198 bp. MCP: fetch_gene_for_expression (handles the centring). Otherwise
fetch a wider locus and pass the TSS as tss_index so the server cuts the
window — but compute that offset on the stripped nucleotide string, not on
file characters.store_inline_sequence, or read the file yourself
for REST. (load_local_fasta exists only in local deployments, not on the
hosted server.)load_demo_sequence(name=...) returns a ready handle
for a keyless smoke test; name is required.See references/sequence-acquisition.md for the exact Ensembl calls and the
expression-window math.
The following transport recipe was tested with mocked responses, not authenticated
inference. Supply a task-appropriate seq before calling it. Use the exact
expression-context wording consistently when comparing predictions.
import os
import time
import requests
BASE = os.environ.get("GI_BASE_URL", "https://api.genomicintelligence.ai").rstrip("/")
HEADERS = {"Authorization": f"Bearer {os.environ['GI_API_KEY']}"}
TASKS = {"promoter", "splice", "enhancer", "chromatin", "annotation", "expression"}
def predict(task, sequence, sequence_name, model=None, options=None, tss_index=None):
if task not in TASKS:
raise ValueError("Unknown GI task")
body = {"sequence": sequence, "sequence_name": sequence_name}
if model is not None:
body["model"] = model
if options is not None:
body["options"] = options
if tss_index is not None:
if task != "expression":
raise ValueError("tss_index is expression-only")
body["tss_index"] = tss_index
r = requests.post(f"{BASE}/v1/tasks/{task}/predict", headers=HEADERS,
json=body, timeout=(10, 300))
r.raise_for_status()
if r.status_code != 200:
raise RuntimeError(f"Unexpected prediction status {r.status_code}")
return r.json()
# After acquiring and checking an appropriate promoter sequence:
# out = predict("promoter", seq, "TP53_region")
# print(out["meta"]["task_specific_counts"]["regions_found"])
# A validated gene-sense expression window, or longer locus with known TSS:
# out = predict("expression", locus_seq, "HBB", tss_index=tss_offset,
# options={"description": "polyA plus RNA-seq; Homo sapiens K562"})
# assert out["meta"]["sequence_length"] == len("".join(locus_seq.split()))
# assert out["meta"]["task_specific_counts"]["scored_window"] == [tss_offset-4599, tss_offset+4599]
# print(out["data"]["prediction"]["expression_log_tpm"])
data.summary is for display: its keys may change without a contract revision.
Use the declared fields in data and meta.task_specific_counts for computation.
A timeout or proxy error may have a non-JSON body; it does not establish that the
inference never ran. Preserve the request ID and avoid blind POST resubmission.
Send Prefer: respond-async on any of the six tasks or the composite. A 202
is {data: {job_id, status: "accepted", links}, meta}. Content-Location and
X-Job-Id identify the same job. Async is JSON-only; text format plus async is
400. Save the job ID before polling. This bounded polling example surfaces
HTTP failures (including 429 and 410) for the caller to handle:
def submit_annotation(sequence, sequence_name):
r = requests.post(f"{BASE}/v1/tasks/annotation/predict",
headers={**HEADERS, "Prefer": "respond-async"},
json={"sequence": sequence, "sequence_name": sequence_name},
timeout=(10, 30))
r.raise_for_status()
if r.status_code != 202:
raise RuntimeError(f"Unexpected submission status {r.status_code}")
return r.json()["data"]["job_id"]
def wait_for_job(job_id, max_polls=120):
if max_polls < 1:
raise ValueError("max_polls must be positive")
for attempt in range(max_polls):
r = requests.get(f"{BASE}/v1/tasks/jobs/{job_id}", headers=HEADERS,
timeout=(10, 30))
r.raise_for_status() # failed job -> its underlying 4xx/5xx, not 200
if r.status_code == 200:
return r.json()
if r.status_code != 202:
raise RuntimeError(f"Unexpected polling status {r.status_code}")
if attempt + 1 < max_polls:
time.sleep(5)
raise TimeoutError(f"Polling stopped; resume this job rather than resubmit: {job_id}")
# job_id = submit_annotation(seq, "TP53_region") # persist this ID
# result = wait_for_job(job_id)
# assert result["data"]["task"] == "annotation"
# transcripts = result["data"]["transcripts"]
200 is completion; 202 contains data.status and data.progress.
Unknown/not-owned jobs are 404; expired jobs are 410 job_expired.
Results are documented as retained 24 hours from last activity; save results
locally. Job listing is a recent, bounded list, not a paginated archive.
On an MCP host, acquire a handle, then predict against it — sequences stay out of the context:
# 1. Acquire a sequence handle (each returns data.ref, passed as sequence_ref):
load_demo_sequence(name="promoter_tp53") # keyless smoke test; name is required
fetch_ensembl_sequence(gene="TP53", flank_bp=5000) # include regulatory context
fetch_region(region="chr11:5,225,000-5,235,000") # coordinates -> handle
fetch_gene_for_expression(gene="HBB") # TSS-centred 9,198 bp handle for expression
# 2. Predict against the handle:
predict_promoter(sequence_ref=<ref>)
predict_expression(sequence_ref=<ref>, description="K562 cells")
predict_splice(sequence_ref=<ref>) # + predict_enhancer / predict_chromatin
# 3. Annotation on MCP is `find_genes` (there is no predict_annotation).
# It takes a handle, not a region, and runs async internally:
find_genes(sequence_ref=<ref>) # wait=True (default) returns the result
find_genes(sequence_ref=<ref>, wait=False) # own key only -> job_id; poll get_job(job_id)
# Acquisition returns data.ref; use that value as sequence_ref.
# Discover models with list_models(task); reference context lives in the
# gi://models, gi://docs/tasks, and gi://account MCP resources.
The shared demo disables get_job, list_jobs, and detached wait=False.
Keep wait=True there; a wait timeout is an error, not a recoverable job handle.
See MCP details for resources, result envelopes and lifetimes.
To answer "what genes are in this region and how are they expressed?", use the composite:
find_genes_and_predict_expression(sequence_ref=..., description=...)
— takes a handle, not a region (acquire one with fetch_region first);
description is required. Finds genes in the sequence and returns an
expression prediction for each.POST /v1/workflows/find-genes-and-predict-expression,
body {sequence, options} with sequence 1,000–500,000 bp and
options.description (cell type / assay) required; a missing or empty
description is a 422 validation_failed. It annotates, centres a 9,198 bp
window on each discovered gene's TSS (padding with N up to half the window
rather than dropping an edge gene), and returns a prediction per gene.
meta.task_specific_counts = {genes_found, genes_predicted, genes_skipped}
with genes_predicted + genes_skipped == genes_found; per-gene causes in
data.expression_predictions[].skip_reason. Above 50,000 bp (its x-sync-limit-bp) it forces
async: a synchronous request over that size is 413 sync_too_large with
error.details = {sequence_length, threshold} — retry the same body with
Prefer: respond-async.The API also publishes a separate, under-development VCF workflow. Its outputs
are not established model results when meta.model is absent; see
the bounded contract note.
| Code | error.code | Meaning | Action |
|---|---|---|---|
| 400 | bad_request | Malformed request | Check the body shape |
| 401 / 403 | unauthorized / forbidden | Missing/invalid key (REST) | Set GI_API_KEY; or use the keyless MCP demo |
| 404 | not_found | Unknown task (/v1/tasks/bogus/predict) or unknown job | Check the task name — an unrecognised task is a 404, not a 422 |
| 413 | payload_too_large | Raw request body over 16 MiB | Split the input — this is the body cap, not the sequence cap |
| 410 | job_expired | Result retention elapsed | Recover saved results or deliberately submit new work |
| 413 | sync_too_large | Synchronous JSON request above the operation's x-sync-limit-bp (200,000 bp on annotation, 50,000 bp on the composite) | Retry with Prefer: respond-async |
| 415 | unsupported_format | Unsupported format query value | Use a format the task supports; there is no silent fallback to JSON |
| 422 | validation_failed | The most common failure: sequence under the task floor or over 500,000 bp, expression below 9,198 bp, a missing/out-of-range tss_index, a missing options.description, or any unknown body or options key; also the splice response cap | Read the message; fix the body |
| 429 | rate_limited / too_many_requests | Rate / concurrency cap | Back off (honour Retry-After); ask GI to raise your tier |
| 5xx | internal_error / service_unavailable / model_loading / timeout | Server error | Preserve request/job IDs; retry polling with backoff, avoid blind POST resubmission |
error.code is a closed 21-value enum (bad_request, unauthorized,
forbidden, not_found, conflict, job_expired, payload_too_large,
sync_too_large, unsupported_format, validation_failed,
too_many_requests, rate_limited, internal_error, timeout,
insufficient_memory, model_not_found, task_not_supported_by_model,
model_loading, service_unavailable, http_error, unknown); treat an
unlisted value as a generic failure, not a parse error.
Branch first on code, never on message text or loc. Pydantic request
failures usually carry details.errors; the splice response cap instead carries
record_count, maximum_records, sequence_length, and threshold. Handle
these as distinct optional detail shapes. More than 20,000 splice records causes
422 validation_failed, not a truncated result; raise the threshold and record
that changed analysis setting. See task caveats.
For correlation, error.request_id and the X-Request-Id header are both
documented on API responses, and success envelopes carry meta.request_id. Reading
the header first remains a safe default.
API responses document RateLimit-Limit, RateLimit-Remaining,
RateLimit-Reset, RateLimit-Policy; a 429 adds Retry-After. The limit is a
burst bucket, not rpm: the published x-rate-limit-burst-divisor is 6, so the
sustained minute allowance is six times that header. Proxy failures may omit
these headers and the usual JSON error envelope.
Reviewed 2026-10-01 against live OpenAPI info.version 2026.09.22.2
(af902d84), x-contract-revision: 16, and gi-mcp 0.1.0a21.
Record the contract revision, resolved model ID, assembly, strand/TSS
provenance, options, and experimental description with results. Hash the exact
submitted bases when available. A handle-only MCP acquisition returns a preview,
not the full bases or a checksum: preserve its acquisition parameters and source
release metadata, and do not invent a hash or claim byte-level verification.
Review evidence and limits distinguish public discovery,
source review, and mocked examples from inference validation.
references/tasks.md — per-task output shapes, model registries, the async
annotation contract.references/api-and-auth.md — REST endpoints, the {data, meta} envelope,
auth, base-URL override, tiers.references/mcp.md — the hosted MCP tool list, the handle-based flow, and the
gi:// resources.references/sequence-acquisition.md — Ensembl fetch calls and the
expression-window (9,198 bp, TSS-centred) math, including tss_index.