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 pharmacoepidemiologygit 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/pharmacoepidemiology)<a href="https://agentmods.dev/skills/awslabs/hcls-agent-skills/pharmacoepidemiology"><img src="https://agentmods.dev/badge/skills/awslabs/hcls-agent-skills/pharmacoepidemiology/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/pharmacoepidemiology"><img src="https://agentmods.dev/badge/skills/awslabs/hcls-agent-skills/pharmacoepidemiology.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 | $0.00227 | $0.05472 |
| Opus 5 | $0.00113 | $0.02736 |
| Sonnet 5 | $0.00045 | $0.01094 |
| Haiku 4.5 | $0.00023 | $0.00547 |
Grade A, and why
pharmacoepidemiology 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 · 365 lines · 227 tokens per session scan A 9e2cc247fc39
pharmacoepidemiology is a skill published in the GitHub repository awslabs/hcls-agent-skills (32 stars, last pushed 10d ago), licensed MIT-0. It adds 227 tokens to every session and 5,472 once invoked, about $0.0011 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
causal-inference
Causal inference for data scientists and analysts — DAGs and do-calculus, propensity score methods, difference-in-differences, instrumental variables, regression discontinuity, synthetic control, and variance reduction techniques (CUPED), using CausalML, DoWhy, and rigorous A/B test analysis.
causal-inference
DoWhy and CausalML toolkit for causal DAG construction, effect identification, ATE estimation, propensity score matching, and refutation testing.
audit-reproducibility
Enforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs. Report PASS/FAIL per claim against tolerance thresholds. Use before submission and before releasing a replication package.
review-paper
Comprehensive manuscript review with three modes: single-pass (default), --adversarial critic-fixer loop, and --peer [journal] simulated peer-review pipeline (editor + 2 dispositioned referees + editorial decision, calibrated to a target journal). R&R continuation via --peer --r2/--r3; hostile-editor stress test via…
grant-proposal
Scaffold a research grant proposal (NSF, NIH, ERC, or foundation) by composing existing primitives — pulls identification strategy from an /interview-me spec, delegates the data-management plan to /data-management-plan and the facilities statement to /capture-environment, and emits a funder-requirements checklist. Use…
preregister
Draft a structured preregistration document (OSF, AsPredicted, or AEA RCT Registry style) from a research spec or free-form study description. Output is a Markdown file with hypotheses, design, sampling plan, analysis plan, exclusions, and inference criteria — annotated with MUST / SHOULD / MAY clarity flags. Use when…