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 digital-pathologygit 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/digital-pathology)<a href="https://agentmods.dev/skills/awslabs/hcls-agent-skills/digital-pathology"><img src="https://agentmods.dev/badge/skills/awslabs/hcls-agent-skills/digital-pathology/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/digital-pathology"><img src="https://agentmods.dev/badge/skills/awslabs/hcls-agent-skills/digital-pathology.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 | $0.00154 | $0.03911 |
| Opus 5 | $0.00077 | $0.01956 |
| Sonnet 5 | $0.00031 | $0.00782 |
| Haiku 4.5 | $0.00015 | $0.00391 |
Grade C, and why
digital-pathology scanned grade C with 1 finding 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 11d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
&& rm -rf /var/lib/apt/lists/* 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 ships with it
4 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.
- 11d ago First seen · 349 lines · 154 tokens per session scan C 782b092cee27
digital-pathology is a skill published in the GitHub repository awslabs/hcls-agent-skills (32 stars, last pushed 10d ago), licensed MIT-0. It adds 154 tokens to every session and 3,911 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gateeval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the backprop attribution is a judgment call.
ablation-design
Design ablation studies to isolate component contributions in ML systems.
venus_finetune_workflow
Fine-tune and run custom protein models on VenusFactory (CSV/HF → config → train → predict). Use when the user brings labeled sequences, wants adapter training (ProtT5/ESM2/Ankh/QLoRA notes), or batch inference with a trained config. Do NOT use for zero-shot mutation without labels (zeroshotmutationworkflow) or…
zero_shot_mutation_workflow
Zero-shot mutation engineering with VenusFactory PLMs. Use when the user wants beneficial mutations, directed evolution candidates, or stability/fitness ranking from a FASTA sequence or PDB structure. Do NOT use for ProteinMPNN inverse folding (proteinmpnndesignworkflow), sequence homology search…
torchdrug
Build and troubleshoot TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.
pyhealth
Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer…