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Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology.
Passed 19 of 19 test cases with the skill, and 8 of 19 without it.
and 15 more in the full report
13 fixed9 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
After submitting an asynchronous remote embedding task using `run_in_context`, which function should be called to block and wait for completion?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends calling monitor_run to wait for job completion | Missed | Passed |
The instructions your model reads when the skill loads.
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.10+ | 3.11 |
| CUDA | 12.1+ | 12.4+ |
| GPU VRAM | 16 GB | 24 GB+ |
scGPT checkpoints are raw directories (args.json, best_model.pt,
vocab.json) — not Hugging Face hub repos. Point at the directory, not an HF
repo id.
from scgpt.tokenizer.gene_tokenizer import GeneVocab
gv = GeneVocab.from_file("/path/to/scgpt-human/vocab.json")
print(len(gv)) # 60697 for the released human checkpoint
import anndata as ad
from scgpt.tasks import embed_data
adata = ad.read_h5ad("dataset.h5ad") # var must contain a gene-name column
emb = embed_data(
adata,
model_dir="/path/to/scgpt-human",
gene_col="feature_name",
use_fast_transformer=False, # see Gotchas
)
# emb is an AnnData with .obsm["X_scGPT"]
test cases passed, vs 8 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.