OpenBioMed is an agent platform and toolkit collection for biomedical research and drug discovery, covering areas such as molecular design, protein analysis, and single-cell data analysis. It is intended for researchers and provides the biomedical skills listed in the catalogue as workflows for Claude Code.
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 PharMolix/OpenBioMed --skill single-cell-foundation-model-scrna-seq-geneformergit clone --depth 1 https://github.com/PharMolix/OpenBioMedWrote 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/pharmolix/openbiomed/single-cell-foundation-model-scrna-seq-geneformer)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-foundation-model-scrna-seq-geneformer"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-foundation-model-scrna-seq-geneformer/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/pharmolix/openbiomed/single-cell-foundation-model-scrna-seq-geneformer"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-foundation-model-scrna-seq-geneformer.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 | $0.00065 | $0.00722 |
| Opus 5 | $0.00032 | $0.00361 |
| Sonnet 5 | $0.00013 | $0.00144 |
| Haiku 4.5 | $0.00006 | $0.00072 |
Grade A, and why
single-cell-foundation-model-geneformer 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Geneformer
Use This Skill When
Use this skill when the task involves official Geneformer workflows such as:
- converting raw scRNA-seq data into Geneformer tokenized datasets
- fine-tuning Geneformer for cell or gene classification
- extracting cell or gene embeddings
- generating state embeddings for downstream perturbation analysis
- running in silico perturbation or in silico treatment style analyses
- distinguishing pretrained zero-shot usage from fine-tuned classifier usage
This skill is for Geneformer-specific workflows, not generic single-cell model use.
Start Here
- Confirm the input is raw-count scRNA-seq data and still suitable for tokenization.
- Check that
ensembl_idandn_countsare available. - Tokenize first unless the user already has a Geneformer
.dataset. - Decide whether the task is classification, embedding extraction, or in silico perturbation.
Choose A Path
Tokenization
Use TranscriptomeTokenizer first for almost every Geneformer workflow.
This step converts raw-count .loom or .h5ad data into tokenized datasets
used by the downstream APIs.
Geneformer expects:
- row attribute
ensembl_id - cell attribute
n_counts
Optional metadata can be passed through during tokenization.
Classification
Use Classifier for:
- cell state classification
- cell type annotation
- gene classification tasks
The input is a tokenized Geneformer .dataset object, not raw AnnData.
Embedding extraction
Use EmbExtractor when the task is to:
- extract CLS, cell, or gene embeddings
- plot or inspect cell embeddings
- generate state embeddings for later perturbation analysis
In silico perturbation
Use InSilicoPerturber for zero-shot or model-based perturbation analyses such as:
- deleting or shifting genes
- modeling start and goal cell states
- ranking perturbations by movement toward a desired cell state
This is one of Geneformer's defining workflows and should be treated as more than ordinary classifier inference.
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 · 99 lines · 65 tokens per session scan A 759724804de4
single-cell-foundation-model-geneformer is a skill published in the GitHub repository PharMolix/OpenBioMed (1,105 stars, last pushed 1mo ago), licensed MIT. It adds 65 tokens to every session and 722 once invoked, about $0.0003 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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