Agent skill · research science · k-dense-ai
geniml
Use Geniml for audited local genomic-interval workflows: validate BED and universe contracts, plan Region2Vec or scEmbed runs, inspect model/tokenizer compatibility, and assess consensus universes.
Why this skill is useful
Provides specific workflows and validation scripts for genomic interval data that the AI wouldn't reliably generate on its own.
What it needs
Requires python, uv installed locally. About 7k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
Geniml Use Geniml for machine learning and statistical workflows over genomic interval sets. Treat coordinates, assemblies, token vocabularies, model artifacts, and sample grouping as explicit contracts. The bundled scripts validate or plan; they do not import Geniml, contact services, deserialize models, or execute training. Bash is declared only for explicit, user-approved uv, Python, Geniml, Gtars, Git, and native CLI commands shown in this guide; bundled Python helpers do not spawn subprocesses. Example paths under data/, refs/, work/, and models/ are user-provided project placeholders, not missing bundled files. Verified release snapshot Latest stable PyPI release on 2026-07-23: geniml==0.8.4 (2026-01-14). PyPI does not declare Requires-Python; its classifiers list Python 3.10-3.14. Prefer Python 3.11 or 3.12 where all native/ML wheels resolve. geniml==0.8.4 accepts gtars>=0.2.5; the verified base smoke used current gtars==0.9.2 (2026-06-17, Python >=3.10). Extras are ml and test. The base install omits Torch, Gensim, Scanpy, Hugging Face Hub, pyBigWig, and HMM dependencies. Upstream documentation contains stale examples. Release source and installed --help output take precedence where they conflict. Install reproducibly Use a project environment and commit its generated lockfile: For Region2Vec, scEmbed, evaluation, or universe methods needing ML libraries: For a durable project, prefer: Do not install an unpinned Git branch. Record Python, OS/architecture, the resolved lockfile, and the PyPI artifact digest. Geniml itself is BSD-2-Clause; the MIT frontmatter value licenses this skill's content. Start with the safety gate Before importing Geniml or running an external binary: 1. Work only with explicit local regular files. Reject URLs, FIFOs, devices, and symlinks unless the user deliberately changes that policy. 2. Validate BED structure and the declared assembly against a trusted local chromosome-sizes file. 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/geniml