Healthcare and Life Sciences Agents Toolkit is an AWS library of domain-specific AI capabilities for healthcare and life sciences workflows, including reference agents, reusable skills, and MCP tools. Builders, researchers, clinicians, scientists, and platform teams use it for tasks such as drug research, clinical trials, genomics, and commercialization. The catalogue add-ons expose parts of the toolkit as portable skills and MCP tools for coding assistants and other platforms.
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 aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill biomarker-multi-agent-discoverygit clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciencesWrote 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/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/biomarker-multi-agent-discovery)<a href="https://agentmods.dev/skills/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/biomarker-multi-agent-discovery"><img src="https://agentmods.dev/badge/skills/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/biomarker-multi-agent-discovery/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/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/biomarker-multi-agent-discovery"><img src="https://agentmods.dev/badge/skills/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/biomarker-multi-agent-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Output Handling · line 27 Output from one security context is used in another without boundary enforcement. Cross-context output flow can leak sensitive information or escalate privileges across trust boundaries.Fix: Enforce strict context boundaries. Do not pass output from one security domain into another without explicit validation and redaction of sensitive content.
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 | $0.00046 | $0.01157 |
| Opus 5.5 | $0.00018 | $0.00463 |
| Sonnet 5.5 | $0.00009 | $0.00231 |
| Haiku 4.5 | $0.00005 | $0.00116 |
Grade A, and why
biomarker-multi-agent-discovery 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 2mo 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.
- 2mo ago First seen · 133 lines · 46 tokens per session scan A 82f589aca5ad
biomarker-multi-agent-discovery is a skill published in the GitHub repository aws-samples/amazon-bedrock-agents-healthcare-lifesciences (275 stars, last pushed 8d ago), licensed MIT-0. It adds 46 tokens to every session and 1,157 once invoked, about $0.0002 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.
biomarker-multi-agent-discovery compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| biomarker-multi-agent-discoveryaws-samples/amazon-bedrock-agents-healthcare-lifesciences | Skill | 275 | 8d ago | MIT-0 | A | 46 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | today | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |