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-scgptgit 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-scgpt)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-scgpt"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-scgpt/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-scgpt"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-scgpt.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 | $0.00163 | $0.00984 |
| Opus 5 | $0.00081 | $0.00492 |
| Sonnet 5 | $0.00033 | $0.00197 |
| Haiku 4.5 | $0.00016 | $0.00098 |
Grade A, and why
alterlab-scgpt 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 12d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
scGPT (single-cell foundation model)
Overview
scGPT (Cui et al., Nature Methods 2024; bowang-lab/scGPT) is a transformer foundation
model pretrained on tens of millions of cells. It provides zero-shot and fine-tuned
cell-type annotation, gene and cell embeddings, batch integration, and
gene-regulatory / perturbation inference — all operating on AnnData (.h5ad) objects.
Its niche vs. the existing single-cell skills: scGPT is the pretrained-transformer route.
For probabilistic latent-variable models use alterlab-scvi-tools; for the conventional
Scanpy analysis pipeline use alterlab-scanpy; scGPT complements both.
When to Use This Skill
Use this skill when the user wants to:
- Annotate cell types with a pretrained foundation model (zero-shot or fine-tuned).
- Generate scGPT embeddings for cells or genes.
- Integrate batches using the transformer's representation.
- Run zero-shot inference / transfer to a new dataset without training from scratch.
Does NOT Trigger
| Scenario | Use instead |
|---|---|
| Probabilistic integration / latent model (scVI, scANVI) | alterlab-scvi-tools |
| Standard QC → cluster → UMAP → differential expression | alterlab-scanpy |
Read/write/wrangle the .h5ad data structure itself |
alterlab-anndata |
| RNA velocity | alterlab-scvelo |
| Protein (not single-cell) language models | alterlab-esm |
Core Capabilities
1. Zero-shot cell embedding & annotation
# bowang-lab/scGPT — API sketch; TODO(verify) against installed scgpt
import scanpy as sc
adata = sc.read_h5ad("cells.h5ad")
# Load a pretrained scGPT checkpoint, embed cells, map to reference cell types.
# (see references/scgpt_usage.md for the exact embed/annotate calls)
Zero-shot mode maps a new dataset onto scGPT's learned space without training — fast triage of cell identities. Fine-tuning on labeled reference data improves accuracy on a specific tissue.
2. Embeddings for downstream analysis
What ships with it
2 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.
- 12d ago First seen · 81 lines · 163 tokens per session scan A ed276271b0f9
alterlab-scgpt is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 8d ago), licensed MIT. It adds 163 tokens to every session and 984 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 2026-08-30.
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