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-journalgit 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-journal)<a href="https://agentmods.dev/skills/huang-sir1/radiology-skills/radiology-journal"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-journal/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-journal"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-journal.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 | $0.00190 | $0.01390 |
| Opus 5 | $0.00095 | $0.00695 |
| Sonnet 5 | $0.00038 | $0.00278 |
| Haiku 4.5 | $0.00019 | $0.00139 |
Grade A, and why
radiology-journal 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Journal Selection & Submission Tiering
Use this skill when the paper is essentially written and the question is where to send it. It reads the manuscript's real strengths and weaknesses, grades it on the dimensions imaging venues actually weigh, and returns an honest reach / target / safety ladder — not a wish list ranked by impact factor.
Core stance
- Fit beats prestige. The best journal is the one whose published pattern matches this paper's design and evidence — not the highest impact factor it might survive.
- Grade honestly on the right axes. External validation, prospectivity, reader/clinical- utility evidence, calibration, sample size, number of centers, and novelty determine tier far more than topic.
- Name the biggest weakness first. The limiting factor (single-center, no external validation, small n, retrospective) sets the realistic ceiling; say it plainly.
- Patterns are durable; current scope is not. Use the publication-pattern heuristics here, but verify each candidate's current aims/scope live (→ radiology-search) before committing.
- A ladder, not a bet. Give reach/target/safety with the trade-off (turnaround, fit, risk), and what to strengthen to move up a tier.
- Integrity. Don't promise acceptance, don't inflate an under-validated paper into a top-tier pitch, and don't pick on impact factor alone.
When to use
- "Where should I submit this?" / "这篇文章适合投哪个期刊？/ 帮我选刊。"
- "Can my single-center retrospective radiomics paper go to Nature Medicine / Lancet Digital Health?"
- "Build me a reach/target/safety submission list."
- "What's the biggest weakness deciding my journal tier, and how do I move up?"
When to open extra files
| File | Open when |
|---|---|
| references/venue-patterns.md | What each imaging/clinical/AI venue tends to publish and the bar it enforces (verify live) |
| references/fit-grading.md | Grading the paper on the tier-deciding dimensions; turning the grade into reach/target/safety |
| references/submission-logistics.md | Article types, format/word/figure limits, cover letter, suggested reviewers, transfer cascades |
| references/venue-style-profiles.md | The user supplies author-guide PDFs/classic articles, asks for journal "taste," or the target is European Radiology / Nature Partner / npj and style/logistics must match the parsed guide profile |
What ships with it
5 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 · 90 lines · 190 tokens per session scan A c0d92a8294ab
radiology-journal is a skill published in the GitHub repository huang-sir1/radiology-skills (1,687 stars, last pushed 1mo ago), licensed MIT. It adds 190 tokens to every session and 1,390 once invoked, about $0.0010 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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