awslabs/hcls-agent-skills is a collection of reusable instructions that help AI agents handle healthcare and life sciences work, including genomics, medical imaging, claims, and drug discovery. It is intended for agents running on Agent Skills-compatible platforms, and the catalogue entries are its individual domain skills.
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 awslabs/hcls-agent-skills --skill cell-type-annotationgit clone --depth 1 https://github.com/awslabs/hcls-agent-skillsWrote 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/awslabs/hcls-agent-skills/cell-type-annotation)<a href="https://agentmods.dev/skills/awslabs/hcls-agent-skills/cell-type-annotation"><img src="https://agentmods.dev/badge/skills/awslabs/hcls-agent-skills/cell-type-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/awslabs/hcls-agent-skills/cell-type-annotation"><img src="https://agentmods.dev/badge/skills/awslabs/hcls-agent-skills/cell-type-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 | $0.00130 | $0.04533 |
| Opus 5.5 | $0.00052 | $0.01813 |
| Sonnet 5.5 | $0.00026 | $0.00907 |
| Haiku 4.5 | $0.00013 | $0.00453 |
Grade A, and why
cell-type-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 1mo 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.
The source is not reproduced here
Licensed MIT-0
The repository is licensed MIT-0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 1mo ago First seen · 346 lines · 130 tokens per session scan A f57e176868c3
cell-type-annotation is a skill published in the GitHub repository awslabs/hcls-agent-skills (68 stars, last pushed 20d ago), licensed MIT-0. It adds 130 tokens to every session and 4,533 once invoked, about $0.0005 per session on Opus 5.5. 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.
Other skills, from other repositories
Single-Cell Analysis Skills Index
Core skills for single-cell RNA-seq analysis: quality control, cell type annotation, and trajectory inference. These are high-priority actionable workflows — load them first for common single-cell tasks.
cellxgene-census-query
Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…
plotly-interactive-plots
Interactive scientific visualization with Plotly. Two APIs: plotly.express (px) for one-liner DataFrame plots, plotly.graphobjects (go) for trace-level control. 40+ chart types with hover, zoom, pan, animation. Exports HTML or static PNG/SVG/PDF via kaleido. Use for volcano plots with gene hover, dose-response…
scvi-tools-single-cell
Deep generative models for single-cell omics: probabilistic batch correction (scVI), semi-supervised annotation (scANVI), CITE-seq RNA+protein (totalVI), transfer learning (scARCHES), and DE with uncertainty. Unified setup→train→extract API on AnnData. Use harmony-batch-correction for fast linear correction without…
nnunet-segmentation
Medical image segmentation with nnU-Net's self-configuring framework — auto-selects architecture, preprocessing, training for any modality. CT, MRI, microscopy, ultrasound in 2D, 3D full-res, 3D low-res, cascade. Pipeline: convert → plan/preprocess → train (5-fold CV) → best config → predict → ensemble. Use when…
arboreto-grn-inference
GRN inference from expression via GRNBoost2 (gradient boosting) or GENIE3 (Random Forest). Load matrix, filter by TFs, infer TF-target-importance links, save network. Dask-parallelized to single-cell scale. Core SCENIC component.