v1.0 to v1.1

---

name: genomic-intelligence

description: "Predict 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."

license: MIT

metadata:

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

---

# Genomic Intelligence — DNA Sequence Models

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](https://docs.genomicintelligence.ai) ·

REST contract at [api.genomicintelligence.ai/v1/openapi.json](https://api.genomicintelligence.ai/v1/openapi.json) ·

hosted MCP server at `https://mcp.genomicintelligence.ai/mcp`

## When to use this skill

Use GI when the user has DNA and wants a model prediction:

- **Find promoters** in a genomic region (`promoter`)

- **Predict splice** donor/acceptor sites (`splice`)

- **Score enhancer activity** — developmental & housekeeping (`enhancer`)

- **Annotate chromatin state** across hundreds of tracks (`chromatin`)

- **Predict expression** as log(TPM+1) from a sequence + cell-type context (`expression`)

- **Annotate genes/transcripts** de novo, no reference needed (`annotation`)

- **Find the genes in a region and predict each one's expression** (composite)

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**.

## Two ways to call GI

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

`references/mcp.md`.

### REST API (universal)

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](#core-rest-workflow).

## Access and authentication

1. The **hosted MCP demo is keyless** — try it with nothing set.

2. The **REST `/v1` API needs a key**, sent as `Authorization: Bearer <key>`.

Request one at [contact@genomicintelligence.ai](mailto:contact@genomicintelligence.ai).

3. **Never hardcode the key.** Read it from the `GI_API_KEY` environment variable

(or a `.env` via `python-dotenv`). Never commit keys.

```bash

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.

## The six tasks

**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`.

## Sequence acquisition

You rarely start from a raw 9,198 bp string. Acquire sequence first:

- **From a gene symbol** → MCP `fetch_ensembl_sequence(gene=...)`; **from

coordinates** → `fetch_region(region=...)`. Both fetch public Ensembl reference

sequence (no key). REST users can query Ensembl REST directly. (`find_genes` is

the annotation task, not an acquisition tool.)

- **For `expression`** → use the TSS-centred fetch so the window is exactly

- **From a local FASTA** → MCP `store_inline_sequence`, or read the file yourself

for REST. (`load_local_fasta` exists only in local deployments, not on the

hosted server.)

- **A demo sequence** → MCP `load_demo_sequence(name=...)` returns a ready handle

See `references/sequence-acquisition.md` for the exact Ensembl calls and the

expression-window math.

## Core REST workflow

```python

import os, requests

BASE = os.environ.get("GI_BASE_URL", "https://api.genomicintelligence.ai")

HEADERS = {"Authorization": f"Bearer {os.environ['GI_API_KEY']}"}

body = {"sequence": sequence, "sequence_name": sequence_name}

if model: body["model"] = model

if options: body["options"] = options

r = requests.post(f"{BASE}/v1/tasks/{task}/predict", headers=HEADERS, json=body)

return r.json() # {"data": {...}, "meta": {...}}

# Promoter:

out = predict("promoter", seq, "TP53_region")

print(out["data"]["summary"])

out = predict("expression", tss_window_9198bp, "HBB",

options={"description": "K562 cells"})

print(out["data"]["prediction"]["expression_log_tpm"])

```

```python

import time

r = requests.post(f"{BASE}/v1/tasks/annotation/predict",

headers={**HEADERS, "Prefer": "respond-async"},

json={"sequence": seq, "sequence_name": "TP53"})

r.raise_for_status() # 202 Accepted

job_id = r.json()["data"]["job_id"]

while True:

j = requests.get(f"{BASE}/v1/tasks/jobs/{job_id}", headers=HEADERS)

if j.status_code == 200: # terminal: body is the final {data, meta}

break

j.raise_for_status() # 202 = still running (2xx, won't raise)

time.sleep(5) # ~20 s typical for ~20 kb

transcripts = j.json()["data"]["transcripts"]

```

## MCP workflow (handle-based)

On an MCP host, acquire a handle, then predict against it — sequences stay out of

the context:

```

# 1. Acquire a sequence handle (each returns a sequence_ref):

fetch_ensembl_sequence(gene="TP53") # gene symbol or Ensembl ID -> handle

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) # -> job_id; poll get_job(job_id)

# Discover models with list_models(task); reference context lives in the

# gi://models, gi://docs/tasks, and gi://account MCP resources.

```

## Composite: find genes, then predict expression

To answer "what genes are in this region and how are they expressed?", use the

composite:

- **MCP:** `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.

## Errors

## Reference files

- `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