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Radiology Research Integrity

CommunityPopular
huang-sir1
radiology-research-integrity

Audit authorship, COI, AI use, selective reporting and artifact integrity; not misconduct adjudication.

Overview

Publisherhuang-sir1
Repositoryradiology-skills
Skill nameradiology-research-integrity
Stars
2K
Forks
20
Bundled files
9
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.

  • 9 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 Research Integrity 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-research-integrity
  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-research-integrity .claude/skills/radiology-research-integrity
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Radiology Research Integrity 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 Research Integrity 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 Research Integrity 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.

Radiology Research Integrity

Use this skill to audit responsible research conduct across proposals, protocols, analyses, manuscripts, figures, presentations and published records. It owns the integrity classification and evidence-preservation packet. It does not decide whether misconduct occurred, investigate a person, contact an institution or publisher, or replace an authorized research-integrity process.

Non-negotiable classification boundary

Use exactly one of these labels for each concern:

  • OBSERVATION — a directly inspected record, file property, version difference or attributed statement, described without motive or accusation;
  • INTEGRITY_SIGNAL — an evidence-anchored inconsistency or pattern that needs clarification, provenance reconstruction or independent review;
  • ALLEGATION — an attributed assertion that prohibited conduct occurred; preserve who made it, when and in what record, but never restate it as fact;
  • INSTITUTIONAL_FINDING — a document-verified determination issued by an authorized institution, funder, regulator, court or publisher. Record its issuer, scope, date, status and exact source.

Do not upgrade one state to another from visual suspicion, model output, similarity software, anonymous commentary, author silence or persuasive language. Honest error and disagreement remain possible until the authorized process determines otherwise.

Choose the mode

ModeUse whenRequired result
triagea possible integrity concern is raised with limited materialbounded classification, preservation actions, missing records and safe next owner
authorship-disclosureauthorship, CRediT, acknowledgements, COI, sponsor role or AI use is disputed or incompleteversioned contribution/disclosure ledger and unresolved author decisions
provenance-audittext, data, code, statistical output, images or figures may not trace to their sourcetransformation chain, discrepancies, claim impact and repair/escalation gate
publication-correctiona preprint, article, abstract, repository or deck may require correction, notice or withdrawalpublication-state map and evidence packet for the authorized publisher/institution
institutional-handoffan integrity signal or allegation needs formal local handlingminimal factual handoff, preserved records, confidentiality boundary and no verdict

For the classification and escalation rules read references/evidence-states-and-escalation.md. For authorship, CRediT, COI, sponsor and AI-use questions read references/authorship-disclosure-and-selective-reporting.md. For data, text, code, DICOM and figure provenance or publication correction read references/provenance-and-publication-record.md. Use references/source-registry.md to verify the authority and currency of policy-facing claims; local and venue rules still require live verification.

Input passport

Record before judging:

request and mode | artifact IDs/versions/digests | study and Claim IDs | public/submitted/private state | institution/jurisdiction/funder/venue | applicable policy and access date | people and roles named by supplied records | evidence locators | confidentiality/access constraints | current safety or publication risk | user authority for any file or external action.

Unknown identities, intent, authorship eligibility, contribution, consent, approval, provenance or policy applicability remain NOT_VERIFIED. Review is read-only unless the user explicitly requests an in-scope artifact edit. Never upload confidential manuscripts or evidence to an external service without authority.

Required workflow

  1. Freeze the review surface. Inventory exact versions, preserve originals and hashes where available, and separate public records from confidential material. Do not ask the user to alter or delete the source record.
  2. Classify, do not accuse. Write atomic rows as OBSERVATION, INTEGRITY_SIGNAL, ALLEGATION or INSTITUTIONAL_FINDING; include source, alternative explanations and what would resolve it.
  3. Audit the relevant chain. Reconcile authorship/contribution/disclosures; protocol and registration to analysis and reporting; raw data to derived values; source images/DICOM to panels; code/config/run to statistics; preprint to version of record and later notices.
  4. Apply the radiology gate. Preserve original pixel/DICOM identity and document window/level, crop, orientation, laterality, resampling, intensity adjustment, annotation, compositing and AI generation/editing. A display transformation is not automatically improper; an undocumented or selectively applied transformation is an integrity signal, not a finding.
  5. Assess claim and safety impact. State which result, participant protection, clinical claim, grant, publication or downstream artifact may be affected. Do not infer intent.
  6. Choose repair or escalation. Ordinary provenance gaps may be repaired with records, correction and transparent disclosure. Potential fabrication, falsification, plagiarism, retaliation, active participant risk, evidence destruction or a material public-record problem triggers STOP_AND_ESCALATE to the authorized owner.
  7. Prepare the bounded packet. Use templates/integrity-audit-report.md and, when relevant, templates/authorship-coi-ai-ledger.md. Preserve neutral language, access controls, provenance and unresolved alternatives.

Stop and escalation gates

Return STOP_AND_ESCALATE when evidence may be destroyed or altered; confidential records cannot be handled safely; participant, animal or public safety may be affected; a protected test or source record has been overwritten; a material publication claim is unreliable; retaliation or coercion is reported; or a formal allegation requires institutional procedure. Name the role, not an invented person: research-integrity officer, institutional counsel, IRB/ethics owner, data-protection owner, funder or publisher as applicable.

The skill may organize evidence and draft a factual handoff. It must not interview witnesses, determine intent, declare guilt/innocence, impose sanctions, promise confidentiality beyond the actual channel, or issue a correction/retraction itself.

Output contract

  1. Audit scope and passport
  2. Concern ledger — stable ID, four-state classification, exact evidence and alternative explanations
  3. Authorship/CRediT/COI/sponsor/AI ledger when applicable
  4. Text-data-code-image provenance map — source, transformation, output and Claim IDs
  5. Selective-reporting and protocol-to-report comparison
  6. Radiology display-integrity gate — original image/DICOM, transformations, laterality and PHI
  7. Claim, participant and public-record impact
  8. Repair / STOP_AND_ESCALATE decision — authorized owner and required evidence, never a verdict
  9. Publication-record action options — correction, retraction or notice remain publisher decisions
  10. Unresolved author/institution inputs and confidentiality limits

Handoffs and non-ownership

  • Human/animal/biosafety approval, consent and authorized conduct -> radiology-ethics.
  • DICOM de-identification, data stewardship, access and sharing -> radiology-data.
  • Statistical validity or selective model evaluation -> radiology-stats / radiology-method-evaluation; whole-manuscript validity -> radiology-prereview.
  • Citation support and retraction-status checking -> radiology-citation; upload package and forms -> radiology-submission; reviewer response -> radiology-response.
  • Frozen executable packaging, prespecified tolerance and independent same-input replay -> radiology-reproducibility; this skill owns neutral classification and escalation when missing, altered or selectively reported run evidence creates an integrity signal, not the replay verdict.
  • Institution, funder and publisher retain investigative, adjudicative and publication authority.

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 Research Integrity AI skill do?

Audit authorship, COI, AI use, selective reporting and artifact integrity; not misconduct adjudication.

Why use Radiology Research Integrity on TypingMind?

Because you install it once and use it with any model. Radiology Research Integrity 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 Research Integrity 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-research-integrity. 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 Research Integrity?

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 Research Integrity?

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

Is the Radiology Research Integrity 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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