--- name: gi-annotation-ab330e76c369 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. Submitted asynchronously — 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), 1,000–500,000 bp (whitespace stripped) — tens to hundreds of kbp is the normal case. 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`. The same models also run interactively at . **Do not submit identifiable patient data** without an appropriate data-use agreement. Key setup: see [Authentication](#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`. The API accepts either delivery mode on every task; this skill always submits async because the pipeline is long-running. The pipeline streams progress through `GET /v1/tasks/jobs/{job_id}` (typically: load → gene-boundaries → gene-intervals → transcripts). > **Contract note.** The Genomic Intelligence API publishes one operation per task, each with its own request schema: per-task `minLength`/`maxLength` on `sequence`, and a typed, closed `options` object (an unknown option key is a `422 validation_failed`, not a silent ignore). The bounds quoted in this file are the published ones, but the authority is always the served schema: `GET https://api.genomicintelligence.ai/v1/openapi.json`. ## Workflow 1. **Parse**: single-record FASTA. 2. **Submit async**: `POST /v1/tasks/annotation/predict` with `Prefer: respond-async` → 202 + `job_id`. 3. **Poll**: stream progress (`percent`, `message`) until terminal. 4. **Render**: `report.md` (transcripts table) + `result.json` (full response) + `reproducibility/`. ## CLI Reference ```bash # 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: 1. `--api-key ` CLI flag (explicit override). 2. `GI_API_KEY` environment variable. 3. 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 (opt-in only). Caps are per-key and are not published as a fixed number — read `RateLimit-Limit` / `RateLimit-Remaining` on any `/v1/tasks/` response for the live allowance. The runner keeps them for you: they are in `result.json` under `rate_limit`, and a `429` names them on the error line. From wherever the ClawBio files live on your machine: ```bash # Repo root (git clone) — or ~/.claude/plugins/cache/clawbio/clawbio// 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: ```bash export GI_API_KEY=gi_yourkeyhere ``` ## Demo ```bash python clawbio.py run gi-annotation --demo ``` Bundled fixture is the TP53 locus (19 kbp). Expect several transcripts — TP53 has multiple annotated isoforms — and a wall time of roughly 20 s. ## Gotchas - **Always submitted async.** This skill sends `Prefer: respond-async` and polls, so it never returns a synchronous response — though the API itself serves annotation synchronously when the header is omitted. Delivery mode is a per-request choice, not a property of the task. - **Length bounds are 1,000–500,000 bp**, published as `minLength` / `maxLength` on `AnnotationPredictRequest` and counted after whitespace is stripped. Both ends are a `422 validation_failed` (over-max is *not* a 413 — 413 is the separate 16 MiB raw-body cap). The skill rejects either locally before spending a request. The 1,000 bp floor is the highest of the six tasks: the gene finder needs a region, not a single exon. - **Long input is normal.** The model handles tens-to-hundreds of kbp up to the 500 kbp cap; longer regions take proportionally more time. Annotation reports `bio_spec.context_window_bp: null` — there is no sliding-window regime caveat here, unlike promoter / splice / enhancer / chromatin. - **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 and development use. Not for clinical or diagnostic decisions. Predicted gene structures are model outputs, not curated reference annotations — for clinical interpretation, anchor to RefSeq / Ensembl. --- Source attribution: ClawBio Original project: https://github.com/ClawBio/ClawBio License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: ClawBio Original project: https://github.com/ClawBio/ClawBio Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Education & Research, Data & Analytics, Development & Engineering, Product Management Roles: Researcher, Data Scientist, ML / AI Engineer, Business Intelligence Analyst, Product Manager Specialties: API Tools: Genomic Intelligence Api Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: 1493497b9aa5a6c5cc104ff4b8f3cf62fa9bb721889116d557efd62211eb2464