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 agentmods add skills/aizech/clinical-skills/dataset-preprocessingnpx skills add aizech/clinical-skills --skill dataset-preprocessinggit 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/dataset-preprocessing)<a href="https://agentmods.dev/skills/aizech/clinical-skills/dataset-preprocessing"><img src="https://agentmods.dev/badge/skills/aizech/clinical-skills/dataset-preprocessing.svg" alt="Measured on agentmods" 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 | $0.00048 | $0.00553 |
| Opus 5 | $0.00024 | $0.00277 |
| Sonnet 5 | $0.00010 | $0.00111 |
| Haiku 4.5 | $0.00005 | $0.00055 |
Grade A, and why
dataset-preprocessing 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 4d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dataset Preprocessing Skill
Triggers
- "preprocess radiology data"
- "DICOM preprocessing"
- "image normalization"
- "data augmentation"
- "quality control pipeline"
- "mask generation"
- "multi-site harmonization"
- "training data preparation"
Parameters
input_format(required): Source data formatdicom- DICOM filesnifti- NIfTI volumesmetadata- Header/excel datamixed- Multiple formats
task_type(required): Downstream ML taskdetection- Object/bounding box detectionsegmentation- Pixel-level segmentationclassification- Image classificationregression- Continuous value prediction
modality(optional): Imaging modalitymulti_vendor(optional): Boolean for multi-site/multi-vendor datadataset_scale(optional): Small (<1K), medium (1K-100K), large (>100K)
Preprocessing Components
Image Processing
- Intensity normalization (z-score, min-max, percentile-based)
- Windowing/leveling for CT/MRI
- Resampling to isotropic voxel size
- Brain extraction (skull stripping)
- Bias field correction for MRI
Quality Control
- Automated quality scoring
- Artifact detection
- Contrast-to-noise ratio
- Resolution verification
- Human-in-the-loop review for edge cases
Augmentation
- Geometric: rotation, flip, scale, elastic deformation
- Intensity: noise, contrast, brightness
- Modality-specific: CT windowing variants, MRI sequence mixing
- Generative: synthetic data augmentation
Format Conversion
- DICOM to NumPy/PyTorch/TensorFlow
- DICOM to NIfTI for volumetric data
- Annotation format conversion (CSV, COCO, YOLO, Pascal VOC)
Output Format
Returns structured JSON with:
- Processing pipeline steps
- Code snippets for each transformation
- Validation checks and statistics
- Expected output specifications
- Common pitfalls and mitigations
Usage Examples
input_format: dicom
task_type: detection
modality: CT
multi_vendor: true
input_format: nifti
task_type: segmentation
dataset_scale: large
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.
- 4d ago First seen · 83 lines · 48 tokens per session scan A daffcf0e4cca
dataset-preprocessing is a skill published in the GitHub repository aizech/clinical-skills (4 stars, last pushed 2mo ago), licensed MIT. It adds 48 tokens to every session and 553 once invoked, about $0.0002 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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