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 aristoteleo/PantheonOS --skill nfcoregit clone --depth 1 https://github.com/aristoteleo/PantheonOSWrote 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/aristoteleo/pantheonos/nfcore)<a href="https://agentmods.dev/skills/aristoteleo/pantheonos/nfcore"><img src="https://agentmods.dev/badge/skills/aristoteleo/pantheonos/nfcore/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/aristoteleo/pantheonos/nfcore"><img src="https://agentmods.dev/badge/skills/aristoteleo/pantheonos/nfcore.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00030 | $0.01143 |
| Opus 5 | $0.00015 | $0.00571 |
| Sonnet 5 | $0.00006 | $0.00229 |
| Haiku 4.5 | $0.00003 | $0.00114 |
Grade A, and why
nf-core Pipelines Skills Index 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 9d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nf-core Pipelines Skills
nf-core is a community-driven collection of 143+ curated Nextflow pipelines for bioinformatics. All pipelines are open-source (MIT), rigorously tested, and run portably on laptops, HPCs, and cloud platforms with automated dependency management via Docker, Singularity, or Conda.
Available Skills
Getting Started & Usage
Installation, configuration, and common usage patterns for running any nf-core pipeline on local machines, HPC clusters, or cloud environments.
Skill file: nfcore_usage.md
When to use:
- First time setting up Nextflow and nf-core
- Configuring pipelines for your HPC cluster or cloud environment
- Understanding resource management, resume, and offline execution
- Looking up nf-core CLI tool commands
Single-Cell & Bulk RNA-seq Pipelines
Pipelines for processing single-cell RNA-seq (10x, Drop-seq, Smart-seq) and bulk RNA-seq data from raw FASTQs to count matrices.
Skill file: nfcore_transcriptomics.md
When to use:
- Processing 10x Chromium, Drop-seq, or Smart-seq scRNA-seq data
- Running downstream single-cell analysis (doublet removal, integration, annotation)
- Processing bulk RNA-seq with STAR, HISAT2, Salmon, or Kallisto
- Generating gene/transcript count matrices and QC reports
Spatial Omics Pipelines
Pipelines for spatial transcriptomics platforms including Visium, Xenium, MERSCOPE, CosMX, and molecular cartography.
Skill file: nfcore_spatial.md
When to use:
- Processing 10x Visium or Visium HD data
- Analyzing Xenium in situ data with cell segmentation
- Running technology-agnostic spatial pipelines (sopa)
- Processing Resolve Bioscience Molecular Cartography data
Epigenomics Pipelines
Pipelines for chromatin accessibility, histone modification, protein-DNA interaction, and DNA methylation profiling.
Skill file: nfcore_epigenomics.md
What ships with it
7 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.
- 9d ago First seen · 138 lines · 30 tokens per session scan A fc9e2f00b977
nf-core Pipelines Skills Index is a skill published in the GitHub repository aristoteleo/PantheonOS (484 stars, last pushed today), licensed BSD-2-Clause. It adds 30 tokens to every session and 1,143 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
histolab
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
torch-geometric
Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
glycobiology
Glycosylation site prediction and glycobiology analysis. N-glycosylation motif finding, O-glycosylation hotspot prediction, glycan structure resources. Lightweight, pure Python. For protein function queries use uniprot-database; for structure analysis use alphafold-database.
pyhealth
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC)…
deepchem
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first…