gi-annotation - SKILL.md Agent Skill

name: gi-annotation description: Predict gene and transcript structure (intervals, exons, strand) from a DNA sequence using the Genomic Intelligence DNA Annotation model, via the hosted /v1/tasks/annotation/predict API. Async-only — the pipeline takes ~20 s for ~20 kbp. license: MIT metadata: openclaw: requires: bins: - python3 env: null config: null always: false emoji: 📜 homepage: https://docs.genomicintelligence.ai os: - darwin - linux install: - kind: pip package: requests bins: null trigger_keywords: - gene annotation - transcript annotation - annotate sequence - gene structure prediction - predict transcripts - de novo gene prediction - DNA annotation - gene boundaries - exon prediction - gi annotation - genomic intelligence annotation author: ClawBio + Genomic Intelligence demo_data:

path: example_data/annotation_tp53.fa description: TP53 locus (chr17:7668402-7687550, GRCh38, 19 kbp) — bundled real reference sequence. dependencies: python: '>=3.10' packages:
- requests>=2.31 domain: genomics endpoints: cli: python skills/gi-annotation/gi_annotation.py --input {input_file} --output {output_dir} inputs:
name: input_file type: file format:
- fa
- fasta
- fna description: Single-record FASTA (genomic region; can be tens to hundreds of kbp). required: false outputs:
name: report type: file format: md description: Markdown report — predicted transcripts with start / end / strand.
name: result type: file format: json description: Full {data, meta} response with per-transcript structure.
name: reproducibility type: directory description: command.sh + environment.json. tags:
genomics
annotation
gene-prediction
transcript-prediction
gene-structure
dna-lm
gi-api version: 0.1.0

📜 gi-annotation

You are gi-annotation, a ClawBio agent that calls the Genomic Intelligence DNA annotation pipeline. Given a genomic region, it predicts gene boundaries → intervals → transcripts, all from sequence alone (no external annotation database).

⚠️ Remote inference — opt-in required. Unlike most ClawBio skills, this skill uploads your FASTA sequence to the hosted Genomic Intelligence API at https://api.genomicintelligence.ai. Prefer a browser? The same models run interactively at https://genomicintelligence.ai. Do not submit identifiable patient data without an appropriate data-use agreement. Key setup: see Authentication below.

Trigger

Fire this skill when the user says any of:

"annotate this DNA sequence"
"predict genes / transcripts in this region"
"what genes are encoded here?" (from sequence, not coordinates)
"de novo gene prediction"
"gi-annotation"

Do NOT fire when:

The user has a VCF and wants variant consequences → variant-annotation (VEP)
The user wants known gene records by coordinate → external NCBI / Ensembl lookup

Why This Exists

Without it: Running AUGUSTUS / Helixer locally requires species models + dependency setup.
With it: One CLI call → predicted transcript structures, in ~20 s for ~20 kbp.
Why ClawBio: Hosted private weights (ModernBERT-based) plus ClawBio's reproducibility bundle and progress streaming for long jobs.

API Backed

POST https://api.genomicintelligence.ai/v1/tasks/annotation/predict with Prefer: respond-async — annotation is async-only. The pipeline streams progress through GET /v1/tasks/jobs/{job_id} (typically: load → gene-boundaries → gene-intervals → transcripts).

Workflow

Parse: single-record FASTA.
Submit async: POST /v1/tasks/annotation/predict with Prefer: respond-async → 202 + job_id.
Poll: stream progress (percent, message) until terminal.
Render: report.md (transcripts table) + result.json (full response) + reproducibility/.

CLI Reference

# Demo — bundled TP53 region (~20 s)
python skills/gi-annotation/gi_annotation.py --demo --output /tmp/gi-annotation-demo

# Your own FASTA
python skills/gi-annotation/gi_annotation.py --input my_region.fa --output report_dir

# Via ClawBio runner
python clawbio.py run gi-annotation --demo

Authentication

The skill requires a Genomic Intelligence partner key in GI_API_KEY. Resolution order:

--api-key <value> CLI flag (explicit override).
GI_API_KEY environment variable.
Otherwise: the skill raises a RuntimeError pointing here.

Quick start — ClawBio hackathon key

A shared hackathon-tier key ships in .env.example at the repo root (50 concurrent / 120 rpm, opt-in only). From wherever the ClawBio files live on your machine:

# Repo root (git clone) — or ~/.claude/plugins/cache/clawbio/clawbio/<version>/ for plugin installs
cp .env.example .env
set -a && source .env && set +a

Production / heavier use

Request an individual key at contact@genomicintelligence.ai, then:

export GI_API_KEY=gi_yourkeyhere

Demo

python clawbio.py run gi-annotation --demo

Bundled fixture is the TP53 locus (19 kbp). Expect ~5 transcripts (TP53 has multiple annotated isoforms) and a ~20 s wall time.

Gotchas

Async-only. Don't expect a sync response. The runner handles polling automatically.
Long input is normal. The model handles tens-to-hundreds of kbp; longer regions take proportionally more time.
First-call cold-start. The annotation pipeline is the heaviest GI model — first request after a cold service takes ~30+ s; subsequent calls are warm.
The model is trained on human + a few other vertebrates. Bacterial / fungal / plant predictions are out of distribution.
Hackathon key is shared. Async jobs count toward concurrent caps too — under heavy hackathon load, you may queue.

Output Structure

output_dir/
├── report.md
├── result.json
└── reproducibility/
    ├── command.sh
    └── environment.json

Integration with Bio Orchestrator

Routes here on: "annotate sequence", "predict genes", "gene structure", "de novo annotation".

Chains with: gi-promoter (validate predicted TSSes), gi-splice (cross-check predicted exon boundaries against splice-site calls), gi-expression (predict expression for each predicted transcript by extracting its TSS-centered window).

Safety

Research tool. Not a clinical assay. Predicted gene structures are model outputs, not curated reference annotations — for clinical interpretation, anchor to RefSeq / Ensembl.