scanpy-cell-type-annotation-starter — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited scanpy-cell-type-annotation-starter (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Use this skill to score marker-gene programs with Scanpy and assign simple cell-type labels on a deterministic toy single-cell matrix.
scanpy.tl.score_genes.scanpy.tl.score_genes before moving to larger reference-mapping workflows.slurm/envs/scanpy/bin/python skills/transcriptomics/scanpy-cell-type-annotation-starter/scripts/run_scanpy_cell_type_annotation.py \
--counts skills/transcriptomics/scanpy-cell-type-annotation-starter/examples/toy_counts.tsv \
--markers skills/transcriptomics/scanpy-cell-type-annotation-starter/examples/toy_markers.json \
--truth skills/transcriptomics/scanpy-cell-type-annotation-starter/examples/toy_truth.tsv \
--summary-out scratch/scanpy-cell-annotation/summary.jsonpython3 -m unittest discover -s skills/transcriptomics/scanpy-cell-type-annotation-starter/tests -p 'test_*.py'accuracy == 1.0 and the predicted label counts split 3 vs 3~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.