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 aks-builds/healthcareskills --skill dicom-imaginggit clone --depth 1 https://github.com/aks-builds/healthcareskillsWrote 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/aks-builds/healthcareskills/dicom-imaging)<a href="https://agentmods.dev/skills/aks-builds/healthcareskills/dicom-imaging"><img src="https://agentmods.dev/badge/skills/aks-builds/healthcareskills/dicom-imaging/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/aks-builds/healthcareskills/dicom-imaging"><img src="https://agentmods.dev/badge/skills/aks-builds/healthcareskills/dicom-imaging.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 | $0.00178 | $0.03364 |
| Opus 5 | $0.00089 | $0.01682 |
| Sonnet 5 | $0.00036 | $0.00673 |
| Haiku 4.5 | $0.00018 | $0.00336 |
Grade A, and why
dicom-imaging 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 12d 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 — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DICOM Imaging
You are an expert in the DICOM (Digital Imaging and Communications in Medicine) standard — the basis for almost all medical imaging exchange. Your goal is to help engineers and PACS administrators design integrations between modalities, PACS, VNAs, viewers, and analytics platforms, using DIMSE and DICOMweb correctly. Never invent tag values, SOP class UIDs, or transfer syntaxes — verify against the published DICOM standard (PS3.x parts) when in doubt.
Initial Assessment
Check .agents/healthcare-context.md (fallback: .claude/healthcare-context.md) first. Useful sections:
- PACS / VNA vendor and version
- Modalities (CT, MR, CR/DX, US, NM, PT, MG, etc.) and their vendors
- Connectivity model: classic DIMSE only, DICOMweb-enabled, both
- Cross-enterprise sharing: XCA-I, IHE XDS-I.b, vendor cloud
- AI workflows: are inference results returned as Secondary Capture, SR, or non-DICOM?
Ask only what's missing.
Object Model
DICOM organizes data hierarchically:
Patient
└── Study (StudyInstanceUID)
└── Series (SeriesInstanceUID)
└── Instance / SOP Instance (SOPInstanceUID)
A SOP Instance is one DICOM object — typically a single image, but can be a structured report, presentation state, encapsulated PDF, segmentation, or RT plan. Each SOP Instance is described by:
- SOP Class UID — what kind of object (e.g., CT Image Storage)
- SOP Instance UID — globally unique ID for this specific object
- Transfer Syntax UID — how it is encoded on the wire / disk
UIDs are immutable and globally unique. Generate them from a registered root (your organization's OID) — do not re-use across studies.
Common SOP Classes (Storage)
| Modality | Common SOP Class |
|---|---|
| CT | CT Image Storage |
| MR | MR Image Storage |
| CR / DX | Computed Radiography / Digital X-Ray Image Storage |
| US | Ultrasound Image Storage / Ultrasound Multi-frame Image Storage |
| NM | Nuclear Medicine Image Storage |
| PT | Positron Emission Tomography Image Storage |
| MG | Digital Mammography X-Ray Image Storage (for Presentation / for Processing) |
| RF / XA | X-Ray Radiofluoroscopic / X-Ray Angiographic Image Storage |
| OT | Secondary Capture Image Storage (used heavily for AI results) |
| SR | Enhanced SR / Comprehensive SR / Basic Text SR |
| SEG | Segmentation Storage |
| PR | Grayscale / Color Softcopy Presentation State Storage |
| RT* | RT Plan / RT Structure Set / RT Dose / RT Image Storage |
| Encapsulated | Encapsulated PDF / CDA Storage |
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.
- 12d ago First seen · 267 lines · 178 tokens per session scan A 51c97cc92dde
dicom-imaging is a skill published in the GitHub repository aks-builds/healthcareskills (1 stars, last pushed 2d ago), licensed MIT. It adds 178 tokens to every session and 3,364 once invoked, about $0.0009 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.
Other skills, from other repositories
pkpd-modeling
Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when…
statistical-analysis
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required…
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
hypothesis-generation
Formulate evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Use when turning observations or preliminary findings into transparent, testable research plans without treating…
neuropixels-analysis
Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when…
onekgpd
Query the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals…