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 aizech/clinical-skills --skill radiology-dataset-guidegit clone --depth 1 https://github.com/aizech/clinical-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/aizech/clinical-skills/radiology-dataset-guide)<a href="https://agentmods.dev/skills/aizech/clinical-skills/radiology-dataset-guide"><img src="https://agentmods.dev/badge/skills/aizech/clinical-skills/radiology-dataset-guide/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/aizech/clinical-skills/radiology-dataset-guide"><img src="https://agentmods.dev/badge/skills/aizech/clinical-skills/radiology-dataset-guide.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 | $0.00056 | $0.00573 |
| Opus 5 | $0.00028 | $0.00287 |
| Sonnet 5 | $0.00011 | $0.00115 |
| Haiku 4.5 | $0.00006 | $0.00057 |
Grade A, and why
radiology-dataset-guide 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 11d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Radiology Dataset Guide Skill
Triggers
- "radiology dataset"
- "medical imaging data"
- "RSNA challenge"
- "MIMIC data access"
- "CheXpert download"
- "dataset comparison"
- "training data preparation"
- "public dataset"
Parameters
task_type(required): ML/AI task being solveddetection- Abnormality/nodule/cancer detectionsegmentation- Organ or lesion segmentationclassification- Disease or finding classificationreconstruction- Image reconstruction/enhancementquantification- Measurement and feature extraction
anatomy(optional): Body region or organ systemmodality(optional): Imaging modality preferenceaccess_requirements(optional): Data use restrictionscommercial_use(optional): Boolean for commercial application intent
Dataset Inventory
| Dataset | Modality | Primary Task | Access | Annotations |
|---|---|---|---|---|
| RSNA Bone Age | X-ray | Regression | Public | Age, quality |
| RSNA Pneumonia | Chest X-ray | Detection | Public | Bounding boxes |
| RSNA Brain Hemorrhage | CT | Detection | Public | Bounding boxes, type |
| NIH ChestX-ray14 | Chest X-ray | Classification | Public | Labels |
| CheXpert | Chest X-ray | Classification | Institutional | Labels |
| MIMIC-CXR | Chest X-ray | Multi | PhysioNet | Labels, reports |
| CheXphoto | Chest X-ray | Classification | Public | Synth/real pairs |
| LUNA16 | CT | Detection | Public | Nodule centers |
| KiTS | CT | Segmentation | Public | Kidney/tumor |
| BraTS | MRI | Segmentation | Research | Multi-modal seg |
| PANDA | Histology | Classification | Public | Biopsy grades |
| OBJ-CXR | Chest X-ray | Detection | Public | Bounding boxes |
Output Format
Returns structured JSON with:
- Relevant datasets ranked by suitability
- Annotation quality and completeness
- Access procedure and requirements
- Key publications and benchmarks
- Preprocessing recommendations
- Compliance and ethics considerations
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.
- 11d ago First seen · 70 lines · 56 tokens per session scan A a3a2bf23eb01
radiology-dataset-guide is a skill published in the GitHub repository aizech/clinical-skills (4 stars, last pushed 2mo ago), licensed MIT. It adds 56 tokens to every session and 573 once invoked, about $0.0003 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 2026-08-31.
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