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Radiology Clinical Domain

CommunityPopular
huang-sir1
radiology-clinical-domain

Set disease pathways, endpoints, reference standards and treatment timelines; not patient care.

Overview

Publisherhuang-sir1
Repositoryradiology-skills
Skill nameradiology-clinical-domain
Stars
2K
Forks
20
Bundled files
11
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.

  • 11 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 Clinical Domain 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-clinical-domain
  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-clinical-domain .claude/skills/radiology-clinical-domain
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Radiology Clinical Domain 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 Clinical Domain 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 Clinical Domain 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.

Clinical domain research router

Use this skill to translate an organ or disease interest into an answerable clinical-imaging research question, or to test whether an existing study respects the clinical pathway that gives its endpoint meaning. It is an independent entry point and a cross-cutting source of clinical context for imaging, radiomics, AI and imaging-mechanism research.

Core stance

  • Begin with the clinical decision and intended use, not with an available feature table or model.
  • Keep screening, diagnosis, differential diagnosis, staging, response, recurrence, prognosis and treatment-effect questions separate. They imply different populations, time zeroes, reference standards, exclusions and claim ceilings.
  • Define the estimand before selecting images or labels. Preserve patient, lesion, vessel, organ, examination and time-point hierarchy; none is automatically a new independent patient.
  • Treat acquisition, reconstruction, contrast phase, scanner/site, treatment and calendar time as possible causes of an apparent signal, not merely covariates to add later.
  • Mark changing staging, reporting, eligibility, diagnostic and treatment rules LIVE_VERIFICATION_REQUIRED. A remembered version or a high-impact paper is not a current rule.
  • Separate AUTHOR_REPORTED, SOURCE_VERIFIED, LOCAL_EXPERT_CONFIRMED and NOT_ASSESSABLE. Never invent a local workflow, guideline version, outcome, treatment rule or specialist consensus.
  • Research support is not individual patient care. Do not recommend diagnosis or treatment for a person; route clinical decisions to qualified responsible clinicians.

Choose the mode

ModeUse whenRequired emphasis
planturning an interest, cohort or biomarker into a studydecision point, PICO/estimand, time zero, endpoint, reference standard, acquisition window, confounders, validation and feasibility
auditreviewing a protocol, Methods, cohort, labels, figures or manuscriptpathway fit, spectrum/verification/incorporation bias, protocol drift, treatment/time leakage, evidence anchors, reviewer red flags and repairs
mentora learner wants direction or alternativesnearest answerable question, assumptions, competing designs, resource tiers, failure modes and decision-bearing questions
interpretreal results are suppliedclinical meaning, transportability, heterogeneity, competing explanations, decision consequence and surviving claim ceiling
writing-handoffthe science is frozen and needs manuscript placementexact clinical facts for title/abstract/Methods/Results/Discussion, live-source status, limitations and forbidden overclaims

Declare one or more active domains: thoracic-lung-oncology, neuro-oncology, liver-hepatobiliary, breast, prostate-pelvic, cardiovascular, acute-emergency, musculoskeletal, pediatric, nuclear-medicine-theranostics, or local-extension-needed.

Use the live-standard receipt whenever a changing clinical or reporting rule affects eligibility, acquisition, category, stage, response, endpoint or a claim.

Required workflow

  1. Read the clinical domain routing contract.
  2. Read only the smallest active-domain playbook:
  3. Build a compact clinical research passport:
    • disease and clinical setting;
    • decision point and intended use;
    • target population, exclusions and recruitment pathway;
    • index image/measurement, comparator and time zero;
    • outcome or target condition, horizon and estimand;
    • independent unit and repeated-measure hierarchy;
    • reference standard, verification window and adjudication;
    • treatment, intervention, surveillance and acquisition timeline;
    • relevant jurisdiction, institution and standard version;
    • representativeness across clinically material subgroups, access to the required imaging/ referral pathway, uncertain/failure-result harms, workflow burden and affected stakeholders.
  4. Mark missing fields AUTHOR_INPUT_NEEDED; do not fill them with plausible defaults.
  5. Run the live-standard gate for every rule that may change. Instantiate the versioned receipt and record issuing authority, normative identifier, full document title, version/edition/effective date, applicability, canonical URL, access date, retrieved artifact path and physical SHA-256, exact section/page/table locator, supported statement and supersedes/superseded_by relation. SOURCE_VERIFIED requires the retrieved artifact and locator, not merely a current landing page. If live access or the normative document fails, preserve LIVE_VERIFICATION_REQUIRED and do not state the rule as current.
  6. Work in order: decision -> PICO/estimand -> cohort pathway -> image protocol -> reference standard -> treatment/time -> analysis unit -> mechanism alternatives -> validation -> claim ceiling.
  7. Return the smallest complete artifact for the selected mode. If a reusable local record helps, use the clinical expert profile.
  8. For any real or proposed use of human participants, identifiable images/records, linked clinical data or human tissue, hand the activity/data map to radiology-ethics: human-subjects. Preserve consent/waiver, privacy, secondary-use, tissue and sharing facts as AUTHOR_INPUT_NEEDED until verified; a clinically sound design is not ethical approval.

Scientific red lines

  • Do not mix a screening cohort with symptomatic, surveillance or treatment-response patients without separate estimands and validation.
  • Do not use downstream pathology, treatment, follow-up or future examinations in a model claimed to operate earlier in the pathway.
  • Pathology is not a single infallible label: record sampling route, target, timing, tissue adequacy, spatial mismatch and whether image findings influenced verification.
  • Excluding indeterminate, unverified, poor-quality or hard-to-biopsy cases can change the target population. Report the flow and consequence rather than calling the remainder “clean”.
  • An imaging phenotype may reflect disease biology, treatment, comorbidity, acquisition or annotation. Name at least one plausible alternative biological and one technical explanation.
  • Prediction or association is not mechanism; prognostic association is not treatment benefit; treatment-group outcome prediction is not a predictive biomarker without a valid comparator and interaction estimand.
  • A standardized reporting category is not automatically a ground-truth disease label, continuous biological quantity or treatment prescription.
  • Internal performance, reader agreement, multi-site transportability, clinical utility, biological concordance and causal validation are distinct rungs; do not rename one as another.

Output contract

Return, in order:

  1. Route and evidence state — mode, active domain, task, supplied materials, live-source status and live-standard receipt IDs/digests.
  2. Clinical research passport — decision point, PICO/estimand, time zero/horizon, unit and pathway.
  3. Protocol and reference-standard map — acquisition/reconstruction, artifacts, label source, verification window and adjudication.
  4. Treatment and time-line map — interventions, disease evolution, imaging windows and leakage or confounding risks.
  5. Equity, access and consequence map — subgroup representation, missing pathway populations, equipment/expertise access, failure/uncertainty harms, workflow burden and stakeholder effects.
  6. Mechanism alternatives and validation ladder — biological, clinical and technical explanations, discriminating evidence and achievable validation tier.
  7. Constructive findings or mentor decisions — exact criterion, evidence anchor, severity, minimum repair, stronger option/cost, closure evidence and learner question.
  8. Claim ceiling and writing handoff — allowed wording, prohibited upgrade, section placement, live citations, normative ID/version/locator, retrieved-artifact SHA-256, superseded relation and residual limitations.
  9. Next action — the single decision-bearing next step plus AUTHOR_INPUT_NEEDED and LIVE_VERIFICATION_REQUIRED fields.

Handoffs and non-ownership

  • Imaging series/phase/sequence qualification, acquisition/reconstruction, quantitative transforms, artifacts, dose, phantom/test-retest and protocol drift route to radiology-acquisition-qc; annotation, radiomics and model execution remain with their corresponding owners. Provide the clinical passport, endpoint, unit and timing contract.
  • Imaging-to-molecular or pathology mechanism interpretation requires the mechanism module; provide disease context, treatment timeline, tissue-image mapping and claim ceiling.
  • Parameter provenance, sensitivity, benchmark fairness and method fit require the method-evaluation module after the clinical estimand is fixed.
  • Estimation, uncertainty, clustered inference, survival analysis and multiplicity require the statistics module with the true unit and estimand preserved.
  • Human participants, identifiable images/records, linked clinical variables, human tissue, consent/waiver, privacy, secondary use and sharing governance require radiology-ethics: human-subjects. Provide population, recruitment, data/tissue flows, linkage/identifiability, sites/jurisdictions and proposed use; this module cannot infer IRB status or approval scope from a published cohort or manuscript sentence.
  • Reader studies, threshold-to-action mapping, prospective workflow evaluation, implementation burden and clinical-utility evidence require radiology-translation. Provide the disease/ pathway, intended-use, reference-standard, treatment-time, subgroup/access and claim-ceiling lock; translation owns the reader/prospective/deployment study design.
  • Full-manuscript review, prose drafting, response letters and submission-file audits remain separate tasks. This skill supplies immutable clinical facts and unresolved live-standard flags.
  • Local expert entries are case-based evidence. They can inform questions and feasibility but cannot silently become universal clinical rules.

Boundary

The six starter routes and four focused playbooks are research-design aids, not clinical-practice guidelines. The pediatric pack may be composed with one organ/pathway pack, but its developmental, body-size, dose, sedation and family-dependence constraints remain explicit. For a different disease, create local-extension-needed from the same passport and live-standard protocol before giving disease-specific advice. No output from this skill substitutes for multidisciplinary review, prospective validation, ethics review or individual patient care.

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 Clinical Domain AI skill do?

Set disease pathways, endpoints, reference standards and treatment timelines; not patient care.

Why use Radiology Clinical Domain on TypingMind?

Because you install it once and use it with any model. Radiology Clinical Domain 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 Clinical Domain 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-clinical-domain. 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 Clinical Domain?

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 Clinical Domain?

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

Is the Radiology Clinical Domain 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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