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Radiology Acquisition Qc

CommunityPopular
huang-sir1
radiology-acquisition-qc

Audit imaging acquisition, reconstruction, series eligibility, quantitative transforms and protocol drift; not model training.

Overview

Publisherhuang-sir1
Repositoryradiology-skills
Skill nameradiology-acquisition-qc
Stars
2K
Forks
20
Bundled files
13
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 13 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by huang-sir1 on GitHub. Read the source before you install it.

Installation

Install the Radiology Acquisition Qc AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/skills/radiology-acquisition-qc
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/huang-sir1/radiology-skills.git /tmp/radiology-skills
mkdir -p .claude/skills
cp -r /tmp/radiology-skills/radiology-skills/skills/radiology-acquisition-qc .claude/skills/radiology-acquisition-qc
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Radiology Acquisition Qc in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Radiology Acquisition Qc on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Radiology Acquisition Qc is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Imaging Acquisition, Reconstruction, and Measurement QC Router

Own the validity of the image as a measurement before annotation, feature extraction, model training, biological interpretation, or clinical deployment. Route to the smallest relevant modality playbook, freeze what was actually acquired and reconstructed, and return the strongest claim the verified measurement chain permits.

When to use

  • "Can these acquisition/reconstruction parameters support a quantitative study?" / "这批影像的采集重建参数能不能支撑定量研究?" Series/phase/sequence selection and exclusion.
  • "Can multi-center, multi-scanner, multi-protocol data be analysed together?" / "多中心、多扫描仪、多协议的数据能不能合并分析?"
  • "How do I interpret artifacts, dose, phantom or test-retest results?" / "伪影、剂量、重复扫描结果怎么解释?"
  • Freeze the measurement passport and claim ceiling before annotation, radiomics or model training.

Ownership

This skill owns:

  • acquisition, reconstruction and post-processing provenance;
  • intended series/phase/sequence/view/tracer selection and exclusion rules;
  • modality-specific artifacts, physical confounders and quantitative-unit validity;
  • phantom, test-retest and reconstruction-perturbation design;
  • protocol/site/scanner/software shift and the acquisition-side harmonisation boundary;
  • an Imaging measurement passport and PASS / CONDITIONAL / STOP measurement claim ceiling.

It does not own disease eligibility or treatment rules, de-identification, ROI annotation, radiomics/DL execution, statistical inference, clinical reader studies, mechanism interpretation, or individual-patient care.

Modes

ModeUse whenOutput emphasis
planDesigning acquisition/QC for a proposed studyminimum defensible protocol, stronger option, repeatability and shift plan
auditImages, DICOM exports, protocols, Methods or QC artifacts existprovenance gaps, series-selection bias, artifacts, sensitivity and claim repair
mentorA learner has a clear acquisition/measurement questiondecision ladder, physical rationale, falsifier, teach-back or transfer prompt
interpretReal QC, phantom, test-retest or protocol-shift results are suppliedmagnitude, uncertainty, failure boundary and surviving measurement claim
writing-handoffAcquisition science is frozenexact Methods/Results/Supplement fields, evidence state and prohibited upgrade

Advisory and tutoring work is read-only. Create or update a passport only when the user requests a durable artifact.

Progressive routing

Resolve the active modality and measurement decision before opening references. Load only the matching file or files:

Active objectLoad
CT, dual-energy/spectral CT, photon-counting CT, CT perfusion or CT-derived quantitative measurementreferences/ct-acquisition-reconstruction.md
Structural/contrast-weighted MRI, DWI/ADC, DCE/DSC/ASL, quantitative mapping or MR reconstructionreferences/mr-acquisition-reconstruction.md
MRS or MRSI, including metabolite ratios, fitted peaks or concentration estimatesMR core plus references/mr-spectroscopy.md
Task or resting-state BOLD fMRI, activation, connectivity or network measurementMR core plus references/functional-mri.md
DTI, DKI, NODDI, fixel analysis, tractometry or tractographyMR core plus references/diffusion-tensor-tractography.md
PET, PET/CT, PET/MR, SPECT, SPECT/CT, SUV, kinetic or count-based measurementreferences/pet-spect-acquisition-quantification.md
Ultrasound, Doppler, CEUS, strain or shear-wave elastographyreferences/ultrasound-ceus-elastography.md
Projection radiography, mobile radiography, mammography, DBT or synthetic 2D mammographyreferences/projection-radiography-mammo-dbt.md
Phantom, test-retest, longitudinal change, multicentre protocol shift, harmonisation or reconstruction sensitivityreferences/phantom-test-retest-protocol-shift.md
Fluoroscopy, DSA/angiography, cone-beam CT, image-guided or interventional imagingLOCAL_EXTENSION_REQUIRED; do not substitute the CT or projection-radiography playbook

For PET/CT load PET/SPECT plus CT; for PET/MR load PET/SPECT plus MR. For multimodal studies, load only each modality that contributes to the requested measurement claim. Add the common phantom/shift playbook when repeatability, longitudinal, cross-scanner, cross-site or absolute quantitative equivalence is material.

Fluoroscopy, DSA/angiography, cone-beam CT and interventional imaging have time-dependent dose, device, subtraction, geometry, procedural and safety contracts not represented here. Mark the route LOCAL_EXTENSION_REQUIRED and stop acquisition-specific assurance until a dedicated, locally verified playbook and responsible radiologist/physicist/procedural owner are supplied. Ordinary CT or projection-radiography rules cannot be borrowed as a substitute.

Required routing workflow

  1. Name the requested measurement object and claim: visual adequacy, relative signal, absolute quantity, longitudinal change, cross-site comparability, reconstruction gain, or downstream input fitness.
  2. Identify the independent hierarchy: patient -> examination/time point -> series/acquisition -> reconstruction/derived object -> frame/slice/voxel/measurement. Do not treat reconstructions, views, frames or repeated scans as independent patients.
  3. Load the modality playbook and reconstruct the acquisition-to-measurement chain from supplied artifacts. Mark unsupported fields AUTHOR_INPUT_NEEDED; do not infer settings from a series description or filename.
  4. Freeze the series/phase/sequence/view/tracer and reconstruction inclusion rule before outcome, model-performance or mechanism results are used to select inputs.
  5. Add the common playbook when a repeatability or transport claim is intended. Distinguish a represented protocol/scanner level from a wholly unseen level and untouched evaluation from adaptation.
  6. Assign PASS, CONDITIONAL or STOP to the exact measurement claim, then hand off the passport without upgrading its evidence state.

Use templates/imaging-measurement-passport.md for a requested persistent/full audit artifact.

Claim ceiling

  • PASS: the acquisition, reconstruction, series rule, QC, units and applicable repeatability/ transport evidence are traceable and fit the stated claim.
  • CONDITIONAL: the measurement remains usable for a narrower population, protocol, relative comparison or sensitivity-bounded claim, with the unresolved source named.
  • STOP: the central measurement is not identifiable or reproducible because the wrong/unknown series, invalid units, result-dependent input selection, dominant unbroken acquisition confounding, failed QC, or an unsupported quantitative transform determines the claim.

A standardized protocol is not proof of cross-platform equivalence. Phantom repeatability is not clinical validity. Harmonisation is not a substitute for missing acquisition metadata, invalid quantification or an outcome-confounded site. A derived map or model output is not a direct physical measurement merely because it is stored as an image.

Handoffs

Requested decisionUnique ownerSend from this skill
disease pathway, endpoint, reference standard, treatment timeline or current clinical ruleradiology-clinical-domainmeasurement passport, protocol limits, artifacts and claim ceiling
cohort inventory, DICOM de-identification, release or repository planradiology-data; governance to radiology-ethicsseries/object inventory and sensitive-data flags without asserting authorization
ROI/VOI or reader annotation procedureradiology-annotationeligible series, geometry, resolution and artifact constraints
radiomics preprocessing/extractionradiology-radiomicsaccepted input series, units, reconstruction/shift and repeatability fields
imaging model/reconstruction network trainingradiology-deep-learningmeasurement target, input provenance, paired reference and protected evaluation rule
parameter comparison, ablation or method-fit judgementradiology-method-evaluationfrozen parameter ledger and acquisition claim; numerical inference to radiology-stats
radiologist reader study, threshold-to-action or prospective workflowradiology-translationintended image presentation, failure/nondiagnostic states and measurement ceiling
imaging-to-pathology/omics mechanismradiology-radiogenomicsphysical contrast candidates, measurement state and technical alternatives

Output contract

Return only the parts needed for the selected mode:

  1. Route and evidence state — mode, modality/subtype, requested measurement and supplied artifacts.
  2. Measurement chain — acquisition -> reconstruction -> post-processing -> selected object -> value.
  3. Series and hierarchy lock — included/excluded objects, unit and repeated-measure structure.
  4. Coverage and disposition denominators — eligible, acquired, exported/received, QC-assessed, accepted, nondiagnostic and excluded counts at the declared grain, with reconciled reasons.
  5. QC and physical-confounder map — artifact, likely effect, evidence, test and disposition.
  6. Repeatability/shift read — when applicable, represented/unseen levels, raw primary analysis, adaptation status and sensitivity.
  7. Claim verdict — PASS / CONDITIONAL / STOP, allowed wording, prohibited upgrade and smallest next action.
  8. Handoff — exact fields and unresolved assumptions for the unique receiving owner.

Boundaries

  • Do not infer a scanner setting, dose, contrast/tracer exposure, sequence, reconstruction, QC pass or quantitative unit from convention.
  • Do not prescribe or alter an individual patient's examination, contrast, radiation, diagnosis or treatment. Local protocol approval, radiation safety and equipment acceptance remain with qualified radiologists, technologists, medical physicists and institutional governance.
  • Do not run a reconstruction, modify source images, or persist a passport unless the requested outcome authorizes that action.
  • Exact standards, profiles and practice parameters are versioned and jurisdiction-dependent. Verify the current authoritative artifact and applicability before asserting conformance.
  • Fluoroscopy, DSA/angiography, cone-beam CT and interventional imaging are LOCAL_EXTENSION_REQUIRED; this skill has no dedicated contract for them and must not upgrade a generic CT or X-ray review into modality- or procedure-specific assurance.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Radiology Acquisition Qc AI skill do?

Audit imaging acquisition, reconstruction, series eligibility, quantitative transforms and protocol drift; not model training.

Why use Radiology Acquisition Qc on TypingMind?

Because you install it once and use it with any model. Radiology Acquisition Qc is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Radiology Acquisition Qc in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/skills/radiology-acquisition-qc. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Radiology Acquisition Qc?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Radiology Acquisition Qc?

As many as you like. As long as a model supports skills, you can use Radiology Acquisition Qc with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Radiology Acquisition Qc AI skill free?

It is published on GitHub by huang-sir1. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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