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Radiology Response

CommunityPopular
huang-sir1
radiology-response

Draft, audit, and revise point-by-point reviewer response letters for Radiology (RSNA) and imaging-journal revisions. Use when the user has reviewer comments / a major or minor revision / 审稿意见 to answer for an imaging-AI/radiomics/radiogenomics manuscript. Assigns each comment a stable ID, classifies it, maps it to a concrete manuscript action, and ties every claimed change to a specific location — without fabricating experiments, analyses, citations, line numbers, or results. Bilingual-aware (中文作者备注 → English response + Chinese confirmation items).

Overview

Publisherhuang-sir1
Repositoryradiology-skills
Skill nameradiology-response
Stars
1.7K
Forks
17
Bundled files
3
LicenseMIT
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.

  • 3 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 Response 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.
  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/modules/radiology-response .claude/skills/radiology-response
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Radiology Response 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 Response 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 Response 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.

Reviewer Response Letters (imaging journals)

Treat the response letter as an editor-facing verification document: every reviewer concern gets an ID, a classification, a concrete action, and a traceable manuscript location.

Core stance

  • Completeness — every comment gets an ID and a response, cross-reference, or an explicit unresolved flag. Nothing ignored.
  • Action mapping — each reply maps to a concrete action: ACCEPT_TEXT, ACCEPT_ANALYSIS, NEW_EXPERIMENT, ADD_RESULT, SOFTEN_CLAIM, CLARIFY, DISAGREE_WITH_REASON, AUTHOR_INPUT_NEEDED.
  • Traceability — every claimed change cites a section/page/line/figure/table/supplement, or a visible placeholder. No vague "we have revised accordingly."
  • Factuality — never invent experiments, analyses, statistics, citations, line numbers, figure panels, editor instructions, or changes not actually made.
  • Tone — cooperative, evidence-forward; disagree only with scientific/scope reasoning, never dismissively.
  • Imaging-aware — common imaging-AI reviewer asks (external validation, leakage, calibration, reader study/MRMC, IBSI reproducibility, subgroup/fairness) routed to the right skill.

When to use

  • "Help me respond to these reviewer comments." / "Draft the rebuttal for this major revision."
  • "Audit my draft response for completeness/tone/traceability."
  • "审稿意见回复" for an imaging manuscript.

When to open extra files

FileOpen when
references/action-mapping.mdClassifying comments and mapping each to a concrete action + manuscript location
references/imaging-reviewer-playbook.mdHandling the recurring imaging-AI/radiomics asks (validation, leakage, calibration, MRMC, IBSI) and difficult cases
references/response-audit-gate.mdFinal audit of a response letter, complex/conflicting reviewer comments, many analysis requests, or when traceability/factuality must be locked before resubmission

Workflow

  1. Intake — split the decision letter into atomic comments; assign stable IDs (R1-1, R1-2, R2-1 …). Note the editor's overall ask.
  2. Classify each (action-mapping.md): major/minor; analysis / clarification / claim / reference / presentation; feasible vs needs author input.
  3. Map to action + location — what changes, where; if it needs a new analysis/experiment, route it (stats / reporting / figure) and mark AUTHOR_INPUT_NEEDED until done.
  4. Draft each response — restate the comment, state the action, quote/point to the revised text + location; calibrate tone; disagree only with reasons.
  5. For final response packages, open response-audit-gate.md and maintain the response ledger before calling the letter complete.
  6. Audit — completeness (every comment answered), traceability (every claim located), factuality (no fabricated change), tone, and consistency across reviewers (conflicting asks reconciled).
  7. Output the letter + an unresolved/author-input list.

Output contract

  1. Response letter — per comment: ID | Reviewer comment (quoted) | Response | Action | Location.
  2. Summary of changes — short editor-facing overview.
  3. Response audit — for final packages: every comment has an action, location, evidence artifact/status, and no unsupported claimed change.
  4. Unresolved / author input needed — comments requiring data/decisions only the author can provide (e.g. "run external validation," "confirm patient-level split").
  5. 待确认(中文) — for Chinese authors, the items needing confirmation.

Never claim a change that was not made. If a requested analysis is not yet done, say so and mark it pending rather than fabricating a result.

Handoffs

New statistics (external validation, DeLong, calibration, MRMC) → radiology-stats; checklist gaps a reviewer cited → radiology-reporting; new/edited prose → radiology-writing/ radiology-polishing; new figures → radiology-figure.

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 Response AI skill do?

Draft, audit, and revise point-by-point reviewer response letters for Radiology (RSNA) and imaging-journal revisions. Use when the user has reviewer comments / a major or minor revision / 审稿意见 to answer for an imaging-AI/radiomics/radiogenomics manuscript. Assigns each comment a stable ID, classifies it, maps it to a concrete manuscript action, and ties every claimed change to a specific location — without fabricating experiments, analyses, citations, line numbers, or results. Bilingual-aware (中文作者备注 → English response + Chinese confirmation items).

Why use Radiology Response on TypingMind?

Because you install it once and use it with any model. Radiology Response 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 Response in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/modules/radiology-response. 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 Response?

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 Response?

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

Is the Radiology Response AI skill free?

Yes. It is published on GitHub by huang-sir1 under the MIT license. 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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