radiology-skills is a collection of Codex skills for medical-imaging research, covering radiomics, deep learning, imaging genomics, multimodal studies, statistics, validation, and scientific publishing. It is intended for researchers who design, analyze, write, and submit medical-imaging AI studies. The catalogue entries are its modular research workflows and specialist advisory 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 huang-sir1/radiology-skills --skill radiology-federated-learninggit clone --depth 1 https://github.com/huang-sir1/radiology-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/huang-sir1/radiology-skills/radiology-federated-learning)<a href="https://agentmods.dev/skills/huang-sir1/radiology-skills/radiology-federated-learning"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-federated-learning/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/huang-sir1/radiology-skills/radiology-federated-learning"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-federated-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00090 | $0.02128 |
| Opus 5 | $0.00045 | $0.01064 |
| Sonnet 5 | $0.00018 | $0.00426 |
| Haiku 4.5 | $0.00009 | $0.00213 |
Grade A, and why
radiology-federated-learning 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Federated Learning for Multi-Center Imaging
Use this skill to design or audit a federated imaging study when raw patient data cannot be pooled. Treat federation as a distributed training architecture—not as automatic privacy, regulatory compliance, fairness, or external validation.
The approved metadata lists horizontal, vertical, split, and personalized federation together, but do not treat them as peer topology choices. Immediately decompose the design into data-ownership, computation, coordination/trust, and output-objective axes as specified below.
Core stance
- Governance before algorithms. Confirm that each site may compute and transmit the planned updates, that controller/processor roles are assigned, and that incident and withdrawal procedures exist. If this is infeasible, stop; do not solve a governance barrier with FedAvg.
- Specify orthogonal design axes. Describe data partition/ownership, computation architecture, coordination/trust, and output objective separately. Horizontal data can use full-model or split computation and can produce one global, clustered, or personalized model.
- Keep a real baseline ladder. Compare local-only models, a federated FedAvg baseline, a centralized pooled-data oracle when lawful or a clearly labeled simulation when not, and the proposed federated method.
- Apply external evaluation as a layer. After development is frozen, evaluate each eligible local, centralized, FedAvg, and proposed model on the same untouched external domain; external validation is not itself a comparator.
- Model non-IID structure explicitly. Scanner vendor, protocol, prevalence, referral pathway, annotation practice, sample size, and outcome availability can all differ by site.
- Define the adversary. Secure aggregation and differential privacy address different threats; neither protects against every leakage, poisoning, or governance failure.
- Separate collaboration from transportability. A center that trained the federation is not external validation, even if it never shared raw data.
What ships with it
3 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.
- 13d ago First seen · 164 lines · 90 tokens per session scan A 3b045462b98c
radiology-federated-learning is a skill published in the GitHub repository huang-sir1/radiology-skills (1,687 stars, last pushed 1mo ago), licensed MIT. It adds 90 tokens to every session and 2,128 once invoked, about $0.0005 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.
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