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 upstream_skillsgit 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/upstream_skills)<a href="https://agentmods.dev/skills/aristoteleo/pantheonos/upstream_skills"><img src="https://agentmods.dev/badge/skills/aristoteleo/pantheonos/upstream_skills/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/upstream_skills"><img src="https://agentmods.dev/badge/skills/aristoteleo/pantheonos/upstream_skills.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 | $0.00000 | $0.01199 |
| Opus 5 | $0.00000 | $0.00600 |
| Sonnet 5 | $0.00000 | $0.00240 |
| Haiku 4.5 | $0.00000 | $0.00120 |
Grade A, and why
upstream_skills 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 13d 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Upstream Skills Index
This directory contains bioinformatics upstream analysis skills for agent-assisted data processing. Each skill provides workflow guidance, code templates, and best practices for specific analysis types.
Available Skills
| Skill | File | Description | Key Use Cases |
|---|---|---|---|
| ATAC-seq | atac.md | Bulk ATAC-seq analysis pipeline | Chromatin accessibility profiling, peak calling, motif analysis |
| RNA-seq | rna.md | Bulk RNA-seq analysis pipeline | Transcriptome profiling, gene expression quantification |
| scATAC-seq | scatac.md | Single-cell ATAC-seq analysis | Single-cell chromatin accessibility, cellranger-atac processing |
| scRNA-seq | scrna.md | Single-cell RNA-seq analysis | Single-cell transcriptomics, cell type annotation with LLM |
| Spatial | spatial.md | Spatial transcriptomics analysis | Visium HD bin-to-cell conversion, spatial cell segmentation |
Skill Summaries
ATAC-seq (atac.md)
Purpose: Complete bulk ATAC-seq data processing from raw FASTQ files to peak calls and downstream analysis.
Key Workflows:
- Quality control with FastQC
- Adapter trimming with Trim Galore
- Genome alignment with Bowtie2/BWA
- BAM filtering and duplicate removal
- Peak calling with MACS2/Genrich
- Coverage track generation
- Motif analysis with HOMER
Tools Used: FastQC, Trim Galore, Bowtie2, BWA, samtools, Picard, MACS2, Genrich, deepTools, HOMER
RNA-seq (rna.md)
Purpose: Complete bulk RNA-seq data processing from raw FASTQ files to gene expression quantification.
Key Workflows:
- Quality control with FastQC
- Adapter trimming
- Genome alignment with STAR/HISAT2
- Expression quantification with featureCounts
- BAM processing and QC
- Coverage track generation
Tools Used: FastQC, Trim Galore, STAR, HISAT2, featureCounts, samtools, MultiQC
scATAC-seq (scatac.md)
Purpose: Single-cell ATAC-seq data processing using 10X Genomics cellranger-atac with downstream analysis.
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.
- 13d ago First seen · 158 lines · 0 tokens per session scan A f9d01bc6456b
upstream_skills is a skill published in the GitHub repository aristoteleo/PantheonOS (485 stars, last pushed today), licensed BSD-2-Clause. It costs nothing until one of its globs matches a file; then it loads 1,199 tokens. 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.
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…
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.
alphafold-database
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.