Skill · em Dados, IA e pesquisa
geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to…
Procedência
- Origem: davila7/claude-code-templates
- Caminho:
cli-tool/components/skills/scientific/geniml - Versão fixada:
57f899e5394bb8ca166f38eacae8f0853cbfe033 - Licença: MIT
- Espelhado em 25/09/2026
- nenhum download no Claude Code Templates (lido em 25/09/2026)
Antes de instalar
6 arquivos · 36,1 KB · só texto, nenhum script
Instalar na sua CLI
O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.
Claude Code
Neste projeto: instala em .claude/skills/geniml/.
d=".claude/skills/geniml" u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/geniml" curl -fsSL --create-dirs \ -o "$d/SKILL.md" "$u/SKILL.md" \ -o "$d/references/bedspace.md" "$u/references/bedspace.md" \ -o "$d/references/consensus_peaks.md" "$u/references/consensus_peaks.md" \ -o "$d/references/region2vec.md" "$u/references/region2vec.md" \ -o "$d/references/scembed.md" "$u/references/scembed.md" \ -o "$d/references/utilities.md" "$u/references/utilities.md" \ -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"
Global: instala em ~/.claude/skills/geniml/.
d="$HOME/.claude/skills/geniml" u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/geniml" curl -fsSL --create-dirs \ -o "$d/SKILL.md" "$u/SKILL.md" \ -o "$d/references/bedspace.md" "$u/references/bedspace.md" \ -o "$d/references/consensus_peaks.md" "$u/references/consensus_peaks.md" \ -o "$d/references/region2vec.md" "$u/references/region2vec.md" \ -o "$d/references/scembed.md" "$u/references/scembed.md" \ -o "$d/references/utilities.md" "$u/references/utilities.md" \ -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"
Codex
Neste projeto: instala em .agents/skills/geniml/.
d=".agents/skills/geniml" u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/geniml" curl -fsSL --create-dirs \ -o "$d/SKILL.md" "$u/SKILL.md" \ -o "$d/references/bedspace.md" "$u/references/bedspace.md" \ -o "$d/references/consensus_peaks.md" "$u/references/consensus_peaks.md" \ -o "$d/references/region2vec.md" "$u/references/region2vec.md" \ -o "$d/references/scembed.md" "$u/references/scembed.md" \ -o "$d/references/utilities.md" "$u/references/utilities.md" \ -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"
Global: instala em ~/.agents/skills/geniml/.
d="$HOME/.agents/skills/geniml" u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/geniml" curl -fsSL --create-dirs \ -o "$d/SKILL.md" "$u/SKILL.md" \ -o "$d/references/bedspace.md" "$u/references/bedspace.md" \ -o "$d/references/consensus_peaks.md" "$u/references/consensus_peaks.md" \ -o "$d/references/region2vec.md" "$u/references/region2vec.md" \ -o "$d/references/scembed.md" "$u/references/scembed.md" \ -o "$d/references/utilities.md" "$u/references/utilities.md" \ -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"
Antigravity
Neste projeto: instala em .agents/skills/geniml/.
d=".agents/skills/geniml" u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/geniml" curl -fsSL --create-dirs \ -o "$d/SKILL.md" "$u/SKILL.md" \ -o "$d/references/bedspace.md" "$u/references/bedspace.md" \ -o "$d/references/consensus_peaks.md" "$u/references/consensus_peaks.md" \ -o "$d/references/region2vec.md" "$u/references/region2vec.md" \ -o "$d/references/scembed.md" "$u/references/scembed.md" \ -o "$d/references/utilities.md" "$u/references/utilities.md" \ -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"
Global: instala em ~/.gemini/antigravity-cli/skills/geniml/.
d="$HOME/.gemini/antigravity-cli/skills/geniml" u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/scientific/geniml" curl -fsSL --create-dirs \ -o "$d/SKILL.md" "$u/SKILL.md" \ -o "$d/references/bedspace.md" "$u/references/bedspace.md" \ -o "$d/references/consensus_peaks.md" "$u/references/consensus_peaks.md" \ -o "$d/references/region2vec.md" "$u/references/region2vec.md" \ -o "$d/references/scembed.md" "$u/references/scembed.md" \ -o "$d/references/utilities.md" "$u/references/utilities.md" \ -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"
Peça ao Rook
Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.geniml
Prévia do SKILL.md
---
name: geniml
description: This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED fil…
---
# Geniml: Genomic Interval Machine Learning
## Overview
Geniml is a Python package for building machine learning models on genomic interval data from BED files. It provides unsupervised methods for learning embeddings of genomic regions, single cells, and metadata labels, enabling similarity searches, clustering, and downstream ML tasks.
## Installation
Install geniml using uv:
```bash
uv uv pip install geniml
```
For ML dependencies (PyTorch, etc.):
```bash
uv uv pip install 'geniml[ml]'
```
Development version from GitHub:
```bash
uv uv pip install git+https://github.com/databio/geniml.git
```
## Core Capabilities
Geniml provides five primary capabilities, each detailed in dedicated reference files:
### 1. Region2Vec: Genomic Region Embeddings
Train unsupervised embeddings of genomic regions using word2vec-style learning.
**Use for:** Dimensionality reduction of BED files, region similarity analysis, feature vectors for downstream ML.
…