Mmcp.market

geniml skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

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.

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Clean: nothing in its files matched our rules. We read 14 files in the folder on 2026-09-28.

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Install the geniml skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/geniml ~/.claude/skills/geniml
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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

3.10-3.14. Prefer Python 3.11 or 3.12 where all native/ML wheels resolve.

  • 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

gtars==0.9.2 (2026-06-17, Python >=3.10).

  • geniml==0.8.4 accepts gtars>=0.2.5; the verified base smoke used current

Hugging Face Hub, pyBigWig, and HMM dependencies.

  • Extras are ml and test. The base install omits Torch, Gensim, Scanpy,

--help output take precedence where they conflict.

  • Upstream documentation contains stale examples. Release source and installed

Install reproducibly

Use a project environment and commit its generated lockfile:

uv venv --python 3.12
uv pip install "geniml==0.8.4" "gtars==0.9.2"

For Region2Vec, scEmbed, evaluation, or universe methods needing ML libraries:

uv pip install "geniml[ml]==0.8.4" "gtars==0.9.2"

For a durable project, prefer:

uv add "geniml[ml]==0.8.4" "gtars==0.9.2"
uv lock

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:

and symlinks unless the user deliberately changes that policy.

  1. Work only with explicit local regular files. Reject URLs, FIFOs, devices,

chromosome-sizes file.

  1. Validate BED structure and the declared assembly against a trusted local

other independent unit—not by BED row or cell alone.

  1. Bound file count, bytes, rows, workers, epochs, and output size.
  2. Separate train/validation/test by patient, donor, biological replicate, or

metadata manifest, and native binaries.

  1. Inventory and checksum the universe, tokenizer, model, config, inputs,

infer approval from a model ID or BEDbase identifier.

  1. Obtain explicit approval before any BEDbase or Hugging Face download. Never

labels, barcodes, and genomic intervals may be sensitive.

  1. Keep logs aggregate and bounded. BED filenames, sample IDs, phenotypes,

Coordinate and assembly contract

BED intervals are normally 0-based, half-open [start, end): start is included, end is excluded, and length is end - start. Do not mix them with 1-based closed coordinates from VCF/GFF or user-facing genome browsers.

For every corpus and artifact, record:

GRCh38.p14), plus the chromosome-sizes checksum;

  • assembly and patch/accession where possible (for example GRCh38 versus

mitochondrial naming;

  • contig naming convention (chr1 versus 1), alt/random/decoy policy, and

BED strand is meaningful;

  • coordinate convention, sorting order, duplicate/overlap policy, and whether

post-liftover validation.

  • liftover tool, chain digest, source/target assemblies, unmapped fraction, and

Reject negative coordinates, end <= start, integer overflow, unknown contigs, ends beyond contig length, malformed columns, mixed assemblies, and silent contig renaming. Sorting and normalization never repair an assembly mismatch. BED3 has no strand; when column 6 is present, preserve +, -, or . unless the assay contract says otherwise.

Run a bounded validation and normalization plan before analysis:

python skills/geniml/scripts/bed_validator.py \
  --input data/peaks.bed \
  --assembly GRCh38 \
  --chrom-sizes refs/GRCh38.chrom.sizes

The validator reports proposed actions but never rewrites the BED file.

Current API map

Region and tokenizer I/O

Prefer Gtars for new interval/tokenizer code:

from gtars.models import Region, RegionSet
from gtars.tokenizers import Tokenizer

regions = RegionSet("data/peaks.bed")
tokenizer = Tokenizer.from_bed("refs/universe.bed")
encoded = tokenizer(regions)
input_ids = encoded["input_ids"]

RegionSet and Tokenizer also accept remote inputs in some constructors; this skill permits local paths only unless network access is explicitly approved. geniml.io.RegionSet(regions, backed=False) remains available as a legacy Python implementation; backed sets are iterable but not indexable. geniml.io.Region uses stop, while gtars.models.Region uses end.

With gtars 0.9.2, seven special tokens are added to a BED vocabulary. Therefore len(tokenizer) is not simply the number of universe rows. Preserve universe row order and the exact special-token map.

Region2Vec

The modern class lives at a concrete module path:

from geniml.region2vec.main import Region2VecExModel
from geniml.region2vec.utils import Region2VecDataset
from gtars.tokenizers import Tokenizer

tokenizer = Tokenizer.from_bed("refs/universe.bed")
dataset = Region2VecDataset("work/tokens.parquet", shuffle=True)
model = Region2VecExModel(tokenizer=tokenizer, embedding_dim=100)
model.train(dataset, epochs=10, window_size=5, num_cpus=4, seed=42)

The Parquet input must contain one list-valued tokens column, one document per row. See references/region2vec.md for export, encoding, legacy CLI, and evaluation details.

scEmbed

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