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 CHENyiru3/AI-Skills-Collections --skill seuratgit clone --depth 1 https://github.com/CHENyiru3/AI-Skills-CollectionsWrote 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/chenyiru3/ai-skills-collections/seurat)<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/seurat"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/seurat/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/chenyiru3/ai-skills-collections/seurat"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/seurat.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 | $0.00056 | $0.02420 |
| Opus 5.5 | $0.00022 | $0.00968 |
| Sonnet 5.5 | $0.00011 | $0.00484 |
| Haiku 4.5 | $0.00006 | $0.00242 |
Grade A, and why
seurat 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 — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Seurat: Single-Cell Analysis (R)
Overview
Seurat is a powerful R package for single-cell RNA-seq analysis, providing a comprehensive toolkit for QC, normalization, dimensionality reduction, clustering, marker gene identification, and multi-modal integration. It is the most widely used R-based single-cell analysis framework.
When to Use This Skill
This skill should be used when:
- Analyzing single-cell RNA-seq data in R
- Performing quality control on scRNA-seq datasets
- Creating UMAP, t-SNE, or PCA visualizations
- Identifying cell clusters and finding marker genes
- Annotating cell types based on gene expression
- Performing multi-modal integration (CITE-seq, ATAC-seq)
- Working with 10X Genomics data (Cell Ranger outputs)
Quick Start
Basic Setup
# Install Seurat (if not already installed)
install.packages("Seurat")
install.packages("SeuratData")
# Load library
library(Seurat)
library(dplyr)
Loading Data
# From 10X Genomics (Cell Ranger output)
data_dir <- "path/to/sample/"
pbmc.data <- Read10X(data.dir = data_dir)
# Create Seurat object
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc", min.cells = 3, min.features = 200)
# From CSV/TSV
data <- read.table("data.csv", sep = ",", header = TRUE, row.names = 1)
pbmc <- CreateSeuratObject(counts = data)
# From h5ad (AnnData)
# Install SeuratDisk package first
library(SeuratDisk)
pbmc <- LoadH5AD("data.h5ad")
Understanding Seurat Object
The Seurat object is the core data structure:
# Access different slots
pbmc@assays$RNA # RNA assay data
[email protected] # Cell metadata (data.frame)
pbmc@reductions # Dimensionality reduction (PCA, UMAP, tSNE)
pbmc@graphs # Neighbor graphs
pbmc@commands # Command history
# Access cell and gene names
colnames(pbmc) # Cell barcodes
rownames(pbmc) # Gene names
# View metadata
head([email protected])
Standard Analysis Workflow
1. Quality Control
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 · 294 lines · 56 tokens per session scan A 261926203814
seurat is a skill published in the GitHub repository CHENyiru3/AI-Skills-Collections (1 stars, last pushed 7d ago), licensed MIT. It adds 56 tokens to every session and 2,420 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-02.
Other skills, from other repositories
torchdrug
Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.
arboreto
Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.
deepspot-m
Generates transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Used for predicted log1p-CPM expression from 224x224 tiles at about 20x, querying the released protein-coding gene panel by symbol, and whole-slide prediction after resolution-aware tiling with histolab.
pyhealth
Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer…
nemo-mbridge-perf-expert-parallel-overlap
Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlapmoeexpertparallelcomm, delaywgradcompute, and flex dispatcher backends such as DeepEP and HybridEP.
evo2
Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring…