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foundation-models

foundation-models is a skill for Claude Code, Codex from zamushwani/biomedical-ai-skills. It costs 0 tokens per session (4,174 once invoked), scanned A, original, MIT.

Guidance on using pretrained machine-learning models for single-cell gene-expression data. It compares models such as scGPT and Geneformer with simpler methods and covers cell embeddings, cell-type labelling, and simulated perturbations.

In plain words
What is it for?
Use it to choose among foundation models, extract cell representations, label cells without task-specific training, fine-tune a model, or predict the effects of gene or drug perturbations.
Why use it?
Large pretrained models can be difficult to choose and may not improve every analysis. This helps decide when one is justified and when a simpler method is a better fit.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 1repo 1mo ago A scan 0 tokens original MIT

Good fit Use it to choose among foundation models, extract cell representations, label cells without task-specific training, fine-tune a model, or predict the effects of gene or drug perturbations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zamushwani/biomedical-ai-skills/foundation-models
View source ↗ zamushwani/biomedical-ai-skills
Install

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.

Any agent
npx skills add zamushwani/biomedical-ai-skills --skill foundation-models
Clone the repo
git clone --depth 1 https://github.com/zamushwani/biomedical-ai-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for foundation-models

README.md
[![agentmods](https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/foundation-models/github.svg)](https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/foundation-models)
Your own site
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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.

agentmods 80×15 button for foundation-models

Your own site · 80×15
<a href="https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/foundation-models"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/foundation-models.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,174 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.04174
Opus 5.5 $0.00000 $0.01670
Sonnet 5.5 $0.00000 $0.00835
Haiku 4.5 $0.00000 $0.00417

Measured 2mo ago against content hash 4268f6fc635e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

foundation-models 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 2mo ago.

The scan reads SKILL.md. This mod also ships 4 executable files (tests/run_all.py, tests/validate_baseline.py, tests/validate_tokenization.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/foundation-models/SKILL.md · 361 lines

How it starts

The opening of the file, as written. The whole thing — 361 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Single-Cell Foundation Models

When to use scGPT, Geneformer, UCE, and the perturbation models, and when a linear baseline beats them. Covers zero-shot embedding extraction, fine-tuning for annotation, in-silico perturbation, and the benchmark evidence behind each recommendation.

When to Use This Skill

Activate when the user requests:

  • scGPT or Geneformer embeddings, fine-tuning, or in-silico perturbation
  • Zero-shot cell type annotation with a pretrained transformer
  • Cross-species or reference-free cell embedding (UCE, TranscriptFormer)
  • Perturbation effect prediction (STATE, Tahoe-x1, GEARS)
  • Advice on whether a foundation model is worth it for a given task

The Short Answer

Default: do not use one.

For human cell type annotation, CellTypist with a matching pretrained model, or scANVI/scVI for joint integration and label transfer, gives an answer in minutes on a CPU or small GPU, with a failure mode you can inspect. Three independent 2026 benchmarks put scVI or plain PCA at or above every foundation model on integration and representation.

The evidence base has converged: single-cell foundation models are representation-strong and prediction-weak.

Where they reliably lose to simple baselines:
  Zero-shot clustering and annotation      vs HVG + PCA, scVI
  Batch integration                        vs scVI, Harmony
  Trajectory inference from embeddings     vs HVG (temporal compression)
  Gene expression reconstruction           vs predicting the mean
  Single-gene perturbation prediction      vs additive / linear models
  GRN inference from attention weights     vs trivial co-expression baselines

That is not a reason to never use one. It is a reason to know which situation you are in.

When to Reach for One

Situation Model Why
<500-1000 labeled cells, large unlabeled target Fine-tuned scGPT whole-human or Geneformer V2-104M The one setting where the pretraining advantage is documented to grow as labels shrink
Query cell types absent from any CellTypist/Azimuth reference scFM embedding + clustering, or UCE zero-shot Reference-free by construction
Non-human or multi-species TranscriptFormer or UCE Shared cross-species latent space
Spatial and dissociated data jointly Nicheformer The only mature spatial foundation model
Combinatorial perturbations, cross-context transfer Arc STATE or Tahoe-x1 The one place deep models beat linear
Cancer / drug response Tahoe-x1, Geneformer-V2-104M_CLcancer, or scGPT pan-cancer Domain-matched pretraining
Bulk RNA-seq to prognosis scFoundation embeddings as extra features Documented C-index gains, low redundancy with expression

Read the full file on GitHub · 361 lines

Files

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.

Changes

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.

  1. 2mo ago First seen · 361 lines · 0 tokens per session scan A 4268f6fc635e

Subscribe to this mod's changes

foundation-models is a skill published in the GitHub repository zamushwani/biomedical-ai-skills (1 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,174 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 2026-08-31.

DESeq2Data and AIScientificai-agentai-skillsbioinformaticscancer-genomicscancer-research
Related

foundation-models compared with skills from other repositories

foundation-models next to 6 skills from other repositories
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