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 zongtingwei/Bioclaw_Skills_Hub --skill cell-annotationgit clone --depth 1 https://github.com/zongtingwei/Bioclaw_Skills_HubWrote 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/zongtingwei/bioclaw_skills_hub/cell-annotation)<a href="https://agentmods.dev/skills/zongtingwei/bioclaw_skills_hub/cell-annotation"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/cell-annotation/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/zongtingwei/bioclaw_skills_hub/cell-annotation"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/cell-annotation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00033 | $0.01087 |
| Opus 5 | $0.00016 | $0.00544 |
| Sonnet 5 | $0.00007 | $0.00217 |
| Haiku 4.5 | $0.00003 | $0.00109 |
Grade A, and why
cell-annotation 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cell Annotation
Version Compatibility
Reference examples assume:
scanpy1.10+celltypist1.6+pandas2.2+
Before using code patterns, verify installed versions match the environment:
- Python:
python -c "import scanpy, celltypist; print(scanpy.__version__, celltypist.__version__)" - If APIs differ, inspect the installed docs and adapt the pattern instead of retrying unchanged.
Overview
Use this skill when the user wants cluster labels or per-cell labels for scRNA-seq. The default stance is:
- inspect markers first
- run reference-based annotation
- keep uncertainty explicit
- export both raw predicted labels and a curated final label column
When To Use This Skill
- clusters already exist and need biological labels
- the dataset has a relevant reference atlas or known marker panels
- the user wants CellTypist or similar automated annotation
Quick Route
- If clusters are unstable or clearly QC-driven, fix preprocessing before annotation.
- If the atlas mismatch is severe, prefer broad lineage labels over overconfident fine labels.
- If multiple methods disagree, mark labels as uncertain instead of forcing a consensus.
Progressive Disclosure
- Read technical_reference.md for strategy selection, confidence interpretation, and disagreement handling.
- Read commands_and_thresholds.md for concrete CellTypist code, score thresholds, and output columns.
Default Rules
- Never accept automated labels without checking marker expression.
- Keep per-cell predictions and cluster-level curated labels separate.
- Use
Unknown,Uncertain, orAmbiguouswhen evidence is weak. - Document the reference model or atlas used.
Expected Inputs
- processed
h5adwith clusters and embeddings - marker gene lists or known lineage markers
- optional reference atlas or model
Expected Outputs
results/annotated.h5adresults/cell_labels.tsvresults/cluster_annotation_summary.tsvfigures/umap_cell_types.pdffigures/marker_dotplot.pdf
What ships with it
3 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 · 155 lines · 33 tokens per session scan A 72ed42034a74
cell-annotation is a skill published in the GitHub repository zongtingwei/Bioclaw_Skills_Hub (26 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 1,087 once invoked, about $0.0002 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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