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 structure-based-drug-designgit 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/structure-based-drug-design)<a href="https://agentmods.dev/skills/awslabs/hcls-agent-skills/structure-based-drug-design"><img src="https://agentmods.dev/badge/skills/awslabs/hcls-agent-skills/structure-based-drug-design/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/structure-based-drug-design"><img src="https://agentmods.dev/badge/skills/awslabs/hcls-agent-skills/structure-based-drug-design.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 | $0.00150 | $0.04214 |
| Opus 5 | $0.00075 | $0.02107 |
| Sonnet 5 | $0.00030 | $0.00843 |
| Haiku 4.5 | $0.00015 | $0.00421 |
Grade A, and why
structure-based-drug-design 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.
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.
- 12d ago First seen · 231 lines · 150 tokens per session scan A 20fc197a6eb1
structure-based-drug-design is a skill published in the GitHub repository awslabs/hcls-agent-skills (32 stars, last pushed 10d ago), licensed MIT-0. It adds 150 tokens to every session and 4,214 once invoked, about $0.0007 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
virtual-screening
Molecular docking and virtual screening for drug discovery. Use for screening compound libraries against protein targets, predicting binding affinities, and identifying lead candidates. Keywords: docking, virtual screening, molecular docking, binding affinity, lead identification.
drug-design
End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.
target-based-lead-design
Generate diverse lead compounds for a specific protein target using structure-based drug design with MolCraft. Use this skill when: (1) Designing drug candidates for a known protein target (PDB ID or disease name), (2) Generating structurally diverse molecules with optimized binding affinity, (3) Filtering candidates…
chem-tools
Computational chemistry workflow guide for DeepChem, PySCF, RDKit, assay-table normalization, PDBbind-style structure datasets, QSAR and structure benchmarks, DrugBank lookup, ligand-only and structure-aware affinity prediction, ADMET triage, bioactivity prediction, virtual screening, and docking follow-up.
docking-tools
Molecular docking workflow guide and reusable pipeline template for AutoDock Vina, Open Babel, and PyMOL.
bio-tools
Bioinformatics workflow guide for sequence analysis, QC, plotting, structure rendering, and literature search.