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Radiology Reader

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huang-sir1
radiology-reader

Build full-paper Chinese-English side-by-side (中英对照), figure/table-aware, source-grounded Markdown readers for imaging-research papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read an imaging paper, make 中英文对照/原文对照/全文翻译解读, extract figures/tables (including imaging panels and result charts) into the right positions, or keep exact source anchors. Especially attentive to radiology specifics: imaging figures, ROC/forest/KM charts, radiomics/AI methods detail, scanner/protocol tables, and statistical reporting. Must not degrade into a summary-only output unless the user explicitly asks for a summary.

Overview

Publisherhuang-sir1
Repositoryradiology-skills
Skill nameradiology-reader
Stars
1.7K
Forks
17
Bundled files
1
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.

  • 1 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 Reader 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-reader .claude/skills/radiology-reader
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Radiology Reader 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 Reader 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 Reader 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.

Full-Paper Markdown Reader (imaging-tuned)

Turn an imaging-research paper into a complete, bilingual, source-grounded Markdown reading artifact. The default output is a paragraph-level 中英对照 companion — not a summary.

Non-negotiable defaults

When the user asks to read/translate a paper, or says 中英对照 / 原文对照 / 全文翻译 / paper reader, produce a paragraph-level bilingual reader by default. Do not replace it with a Chinese-only summary, a highlights list, or captions without figure crops.

What to preserve (and why it matters for imaging papers)

  • Full prose, paragraph structure, and section flow (incl. Materials and Methods detail — scanner/protocol, segmentation, model/feature pipeline, statistical analysis).
  • Original + faithful Chinese translation at block level; keep technical terms, gene/model names, units, p-values, CIs, and citation markers intact.
  • Figures and tables placed near their first substantive mention. Crop tightly:
    • Imaging panels — keep windowing/arrow annotations visible; note the modality/sequence.
    • Result charts (ROC, calibration, forest, Kaplan-Meier, DCA) — keep axes/legend legible.
    • Tables (cohort characteristics, scanner parameters, performance) — keep near the interpreting paragraph.
  • Stable anchors on every block (S### body, C### captions, F### figures, T### tables).

When to open extra files

FileOpen when
references/structured-reading-notes.mdThe user is reading for literature review, paper comparison, journal club, gap discovery, manuscript writing, or wants craft/figure/limitation patterns rather than translation only

Workflow

  1. Identify source & paper type (DTA / prediction model / radiomics / radiogenomics / review) — this sets how tightly to couple text, figures, and stats.
  2. Build a full source map before translating (page, block type, original, translation, reading order, nearby figure/table, confidence). Process the whole document, not just the abstract.
  3. Translate conservatively — meaning not style; keep Methods/stats detail; mark uncertain OCR rather than guessing; never drop limitations / data-availability / ethics.
  4. Extract & place figures/tables at first substantive mention; tight crops; keep caption
    • Chinese caption; add a one-line reading note (what to inspect — e.g. "AUC and CI in panel A; calibration in panel B").
  5. For literature-review or journal-club reading, open structured-reading-notes.md and create Pass 1/2/3 notes at the depth the task deserves.
  6. Generate paper.md (primary) + source_map.json + translation_notes.md + assets/. Add a terminology table for recurring imaging/AI terms.
  7. Answer follow-ups from the source with block IDs + page numbers; don't answer from memory.

Block shapes

markdown
<a id="S001"></a>
**Source:** p.1 S001
**Original:** [source paragraph]
**中文:** [faithful translation]
markdown
<a id="F001"></a>
### Fig 1. [short translated title]
**Placed near:** p.3 S012  **Source:** p.4 C001
![Fig 1](assets/fig1.png)
**Original caption:** [...]
**中文图注:** [...]
**Reading note:** [what to inspect — e.g. modality/window; which metric/CI]

Output contract

  • paper.md with **Original:**/**中文:** pairs for all substantive blocks.
  • Every figure/table in assets/ has a Markdown block + source pointer; every link resolves.
  • source_map.json parses; translation_notes.md records skipped/uncertain/draft content.
  • If structured reading was requested: paper note with depth level, claim-evidence table, and craft/positioning notes where relevant.
  • Don't hide missing content — label draft mode.

Tooling & handoffs

  • PDF extraction/OCR → load the pdf skill first.
  • Citation export of the paper's references → radiology-citation.
  • Want a journal-club deck instead → radiology-paper2ppt.
  • Quality bar: feels like a paper reader, not a machine-translation dump; reader can move between original ↔ translation ↔ source location ↔ figure/table evidence.

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 Reader AI skill do?

Build full-paper Chinese-English side-by-side (中英对照), figure/table-aware, source-grounded Markdown readers for imaging-research papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read an imaging paper, make 中英文对照/原文对照/全文翻译解读, extract figures/tables (including imaging panels and result charts) into the right positions, or keep exact source anchors. Especially attentive to radiology specifics: imaging figures, ROC/forest/KM charts, radiomics/AI methods detail, scanner/protocol tables, and statistical reporting. Must not degrade into a summa...

Why use Radiology Reader on TypingMind?

Because you install it once and use it with any model. Radiology Reader 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 Reader 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-reader. 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 Reader?

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 Reader?

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

Is the Radiology Reader 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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