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-langcellgit 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-langcell)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-foundation-model-scrna-seq-langcell"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-foundation-model-scrna-seq-langcell/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-langcell"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-foundation-model-scrna-seq-langcell.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 | $0.00067 | $0.00737 |
| Opus 5 | $0.00034 | $0.00368 |
| Sonnet 5 | $0.00013 | $0.00147 |
| Haiku 4.5 | $0.00007 | $0.00074 |
Grade A, and why
single-cell-foundation-model-langcell 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangCell
Use This Skill When
Use this skill for the local LangCell project at /DATA/disk0/zhaosy/home/LangCell.
It is the right choice when the task involves:
- zero-shot cell identity or cell type annotation from tokenized single-cell data
- few-shot cell type annotation with very limited labels
- finetuning only the LangCell cell encoder (
LangCell-CE) - preprocessing AnnData into the tokenized format expected by LangCell
- preparing text descriptions for candidate cell identities
- understanding how LangCell combines cell embeddings and text embeddings
Do not use this skill for ordinary Scanpy analysis that does not depend on LangCell.
Start Here
- Confirm whether the user wants zero-shot annotation, few-shot annotation, or cell-encoder-only finetuning.
- Check that the input is already tokenized, or route through preprocessing first.
- Check whether the required external assets exist: checkpoints, tokenized dataset, ontology / text-description JSON.
- Prefer the zero-shot path first if the user is exploring LangCell rather than benchmarking a supervised baseline.
Choose A Path
Zero-shot annotation
Start here for most LangCell usage. The defining behavior is:
- encode cells with
cell_bert + cell_proj - encode candidate texts with
text_bert + text_proj - score cell-text matches with
ctm_head - combine similarity and matching scores for final predictions
Use LangCell-annotation-zeroshot/zero-shot.ipynb as the primary reference path.
Few-shot annotation
Use LangCell-annotation-fewshot/fewshot.py when only a tiny labeled support set
is available and the user still wants the multimodal LangCell path.
LangCell-CE finetuning
Use LangCell-CE-annotation/finetune.py when the user wants a standard
supervised classifier on top of the pretrained cell encoder.
Preprocessing
LangCell does not take raw .h5ad directly for these downstream scripts. First:
- read AnnData with
scanpy - add
obs["n_counts"] - ensure
var["ensembl_id"]exists - tokenize with
LangCellTranscriptomeTokenizer - save with
save_to_disk(...)
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 · 79 lines · 67 tokens per session scan A 68d45fd312e3
single-cell-foundation-model-langcell is a skill published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 737 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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