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 ngs-quality-controlgit 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/ngs-quality-control)<a href="https://agentmods.dev/skills/awslabs/hcls-agent-skills/ngs-quality-control"><img src="https://agentmods.dev/badge/skills/awslabs/hcls-agent-skills/ngs-quality-control/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/ngs-quality-control"><img src="https://agentmods.dev/badge/skills/awslabs/hcls-agent-skills/ngs-quality-control.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 | $0.00058 | $0.03839 |
| Opus 5 | $0.00029 | $0.01920 |
| Sonnet 5 | $0.00012 | $0.00768 |
| Haiku 4.5 | $0.00006 | $0.00384 |
Grade A, and why
ngs-quality-control 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 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.
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 ships with it
1 file 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 · 289 lines · 58 tokens per session scan A 65467f4040b9
ngs-quality-control is a skill published in the GitHub repository awslabs/hcls-agent-skills (32 stars, last pushed 10d ago), licensed MIT-0. It adds 58 tokens to every session and 3,839 once invoked, about $0.0003 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.
Other skills, from other repositories
genomics-qc
Load when running pre-alignment FASTQ quality control — Phred quality scores, Q20/Q30 rates, GC / N content, read-length distribution, adapter-contamination detection. Skip when working with already-aligned BAMs (use genomics-alignment); peak / variant files are the input (use the relevant downstream skill).
skills
This directory contains reusable pipeline templates for the runqcpipeline tool.
smiles-validation
Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.
bias-detection
Assess systematic biases in the evidence body — publication bias, reporting bias, and selective outcome reporting. Budget: 40 studies, 40 effect sizes, 40 web searches.
assumption-audit
Surface all assumptions, classify by vulnerability (load-bearing × likely-false), validate causal logic. Focus on dangerous assumptions — high load-bearing + non-explicit.
anomaly-characterization
SOP: Describe and classify anomalous phenomena that existing theory cannot explain.