Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add silverstein/claude-scientific-skills-desktop --skill genimlgit clone --depth 1 https://github.com/silverstein/claude-scientific-skills-desktopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/geniml)<a href="https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/geniml"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/geniml/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/geniml"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/geniml.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00089 | $0.02342 |
| Opus 5 | $0.00044 | $0.01171 |
| Sonnet 5 | $0.00018 | $0.00468 |
| Haiku 4.5 | $0.00009 | $0.00234 |
Grade A, and why
geniml scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to geniml — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
uv uv pip install geniml
For ML dependencies (PyTorch, etc.):
uv uv pip install 'geniml[ml]'
Development version from GitHub:
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.
Workflow:
- Tokenize BED files using a universe reference
- Train Region2Vec model on tokens
- Generate embeddings for regions
Reference: See references/region2vec.md for detailed workflow, parameters, and examples.
2. BEDspace: Joint Region and Metadata Embeddings
Train shared embeddings for region sets and metadata labels using StarSpace.
Use for: Metadata-aware searches, cross-modal queries (region→label or label→region), joint analysis of genomic content and experimental conditions.
Workflow:
- Preprocess regions and metadata
- Train BEDspace model
- Compute distances
- Query across regions and labels
Reference: See references/bedspace.md for detailed workflow, search types, and examples.
3. scEmbed: Single-Cell Chromatin Accessibility Embeddings
Train Region2Vec models on single-cell ATAC-seq data for cell-level embeddings.
Use for: scATAC-seq clustering, cell-type annotation, dimensionality reduction of single cells, integration with scanpy workflows.
Workflow:
- Prepare AnnData with peak coordinates
- Pre-tokenize cells
- Train scEmbed model
- Generate cell embeddings
- Cluster and visualize with scanpy
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 313 lines · 89 tokens per session scan A 87aa6a1cc63d
geniml is a skill published in the GitHub repository silverstein/claude-scientific-skills-desktop (22 stars, last pushed 5mo ago), licensed MIT. It adds 89 tokens to every session and 2,342 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to geniml, differing in 5 lines, and is treated as a copy.
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