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 AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-genimlgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote 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/alterlab-ieu/alterlab-academic-skills/alterlab-geniml)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-geniml"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-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/alterlab-ieu/alterlab-academic-skills/alterlab-geniml"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-geniml.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 28 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
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 | $0.00152 | $0.03178 |
| Opus 5 | $0.00076 | $0.01589 |
| Sonnet 5 | $0.00030 | $0.00636 |
| Haiku 4.5 | $0.00015 | $0.00318 |
Grade A, and why
alterlab-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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 299 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
Verified against geniml 0.8.4. Install with uv (prefer uv run --with for one-off runs so nothing leaks into the project env):
uv pip install 'geniml[ml]' # [ml] pulls torch/gensim; needed for region2vec/scembed
scEmbed and scATAC-seq examples also need scanpy: uv pip install scanpy. Universe building (build-universe) and hard tokenization shell out to external binaries — uniwig (coverage tracks) and bedtools — so install those separately.
Development version: uv pip install git+https://github.com/databio/geniml.git
Import paths (IMPORTANT — verified gotcha)
geniml's subpackage __init__.py files do not re-export their internals, so the obvious short imports fail with ImportError. Import from the concrete module instead:
| Want | Wrong (fails) | Correct (verified) |
|---|---|---|
| Hard tokenization | from geniml.tokenization import hard_tokenization |
from geniml.tokenization.main import hard_tokenization_main |
| Region2Vec (legacy fn) | from geniml.region2vec import region2vec |
from geniml.region2vec.main_legacy import region2vec |
| Region2Vec (model class) | — | from geniml.region2vec.main import Region2VecExModel |
| scEmbed | from geniml.scembed import ScEmbed |
from geniml.scembed.main import ScEmbed |
| Token dataset | from geniml.io import tokenize_cells (does not exist) |
from geniml.region2vec.utils import Region2VecDataset |
| Tokenizer | — | from gtars.tokenizers import Tokenizer |
Tokenizing cells is handled internally by ScEmbed via a gtars Tokenizer; there is no geniml.io.tokenize_cells function.
What ships with it
6 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.
- 6d ago First seen · 299 lines · 152 tokens per session scan A 6f4e0f877186
alterlab-geniml is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 7d ago), licensed MIT. It adds 152 tokens to every session and 3,178 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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