geniml
by K-Dense-AIgeniml is a skill for genomic interval machine learning on BED files, scATAC-seq outputs, and chromatin accessibility data. Use it for Region2Vec, BEDspace, scEmbed, consensus peaks, and other region-level ML workflows. It is a good fit when you need embeddings, clustering, or preprocessing guidance for genomic regions.
This skill scores 78/100, which means it is a solid listing candidate for directory users: it has a clear genomic-interval ML scope, concrete workflows, and enough operational detail to justify installation, though it still leaves some setup and adoption gaps compared with a fully packaged skill.
- Explicit trigger coverage for BED/genomic interval ML tasks, including Region2Vec, scEmbed, universes, and consensus peaks.
- Substantial workflow content with multiple headings, code fences, and repo/file references, giving agents more to act on than a generic prompt.
- Includes install commands and a clear package identity for users evaluating whether it fits their genomic data workflow.
- No scripts, references, resources, or rules files are included, so agents may need to infer some implementation details from prose alone.
- The skill points to a GitHub install path and a Python package install, but there is no dedicated quick-start or validation checklist to reduce setup guesswork.
Overview of geniml skill
What geniml is for
The geniml skill helps you work with genomic interval data as machine-learning input, especially BED files, scATAC-seq outputs, and chromatin accessibility regions. It is best for readers who need to turn raw genomic intervals into embeddings, clusters, or other ML-ready features rather than just annotate or visualize them.
When it is a good fit
Use the geniml skill when your job is to build region representations, compare interval sets, define consensus peaks, or run downstream modeling on interval collections. It is especially relevant for geniml for Data Analysis workflows that center on Region2Vec, BEDspace, scEmbed, and universes-based peak handling.
What matters most before install
The main decision point is whether you need a specialized genomic-interval ML workflow, not a generic Python prompt. If your task is simply filtering BED files, calling peaks, or doing standard bioinformatics QC, geniml is probably too specialized. If you need embeddings or region-level ML features, geniml install is worth it.
How to Use geniml skill
Install the skill and check the package path
Install the skill in your agent environment with the project’s skill manager, then point your workflow at the repository path scientific-skills/geniml. After install, confirm the geniml skill is available before drafting prompts that depend on it.
Read the right files first
Start with SKILL.md, then inspect the sections it points to for installation, core capabilities, and the method you actually need. In this repository, there are no extra scripts/, rules/, or resources/ folders, so the main value is in the skill body itself and the links it embeds.
Give the model the right input shape
A strong geniml prompt says what kind of intervals you have, what format they are in, and what output you want. For example: “Use the geniml skill to convert these BED files into region embeddings for clustering, and tell me what preprocessing assumptions matter.” That is better than “analyze my genomics data” because it gives the skill a concrete target.
Practical workflow for better output
Use geniml usage in three steps: define the interval source, choose the method, then constrain the result. Include the organism, file count, region definition, and whether you want embeddings, consensus peaks, or cell-level representations. If the task involves ML dependencies, mention that early so the output can account for geniml[ml] and PyTorch-style setup.
geniml skill FAQ
Is geniml only for BED files?
Mostly yes. The geniml skill is centered on genomic intervals, so BED files and related region tables are the natural fit. It may touch other inputs, but if your data is not interval-based, another tool is probably a better match.
Do I need machine learning experience to use it?
No, but you do need a clear objective. Beginners can use the geniml guide if they can describe their data and desired output in plain language. The harder part is not syntax; it is choosing the right region-learning workflow.
How is geniml different from a normal prompt?
A normal prompt usually asks for a generic explanation. The geniml skill is better when you need workflow-specific guidance, such as how to prepare interval data, which model family to use, and what assumptions affect downstream embeddings or clustering. That makes it more useful for reproducible analysis.
When should I not use geniml?
Do not use geniml for simple BED editing, genome browser tasks, or non-interval ML problems. If you are not trying to learn representations from genomic regions, the skill adds overhead without much benefit.
How to Improve geniml skill
Specify the analysis target
The fastest way to improve geniml output is to name the exact task: Region2Vec embeddings, BEDspace comparison, scEmbed analysis, or universe construction. The skill performs better when it knows whether you want similarity, clustering, cell-level features, or consensus regions.
Provide data constraints up front
Tell the model how many files you have, whether the intervals come from bulk or single-cell data, and whether the regions are fixed-width or variable-width. These details change preprocessing choices and help the geniml skill avoid vague advice.
Ask for the workflow, not just the result
A good geniml usage request asks for steps, required inputs, and likely pitfalls. For example: “Show me the geniml guide for training embeddings from BED files, and note what I need to standardize before training.” This gets you more actionable output than asking for a one-sentence summary.
Iterate using method-specific feedback
If the first answer is too broad, narrow it by asking for the exact method and the missing decision points. For geniml for Data Analysis, that usually means clarifying universe selection, tokenization assumptions, embedding goals, and whether you need ML dependencies installed before proceeding.