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Radiology Search

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huang-sir1
radiology-search

Multi-source literature, dataset, and local-corpus search for imaging research, with deduplication, identifier verification, reference management, and survey/RAG workflow support. Searches PubMed, arXiv, Crossref, and imaging dataset registries (TCIA, GEO, cBioPortal, OpenNeuro, Grand Challenge, Medical Segmentation Decathlon), and can structure local paper/RAG workflows when a paper database, PaperQA2, NotebookLM, or paperpipe-style backend exists. Use when the user wants to find papers/datasets, run systematic or survey-level search, verify DOI/PMID/arXiv IDs, map a field, build a gap matrix, or synthesize literature for a manuscript. Verifies identifiers and exposes incomplete metadata; never fabricates bibliographic fields.

Overview

Publisherhuang-sir1
Repositoryradiology-skills
Skill nameradiology-search
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 Search 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-search .claude/skills/radiology-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Radiology Search 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 Search 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 Search 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.

Multi-Source Search (imaging literature + datasets)

Find and verify imaging-research papers and datasets across the right sources, merge without double-counting, and hand clean candidates to citation/export.

Core stance

  • Right source first. PubMed for biomedical recall (+ MeSH); arXiv for imaging-AI methods and preprints; Crossref for DOI/cross-disciplinary metadata. Dataset registries for data.
  • Verify identifiers (DOI/PMID/arXiv) before citing; expose failed/missing metadata.
  • Deduplicate by DOI → PMID → arXiv ID → normalized title; don't count duplicates as independent evidence.
  • Recall vs precision — for systematic/DTA reviews use MeSH + structured strategy and log it (PRISMA-DTA reproducibility); for quick lookups, precision.
  • No fabrication — never invent volume/issue/pages/DOI/dataset accession.

When to use

  • "Find recent papers on [imaging-AI topic]." / "Systematic search for a DTA meta-analysis."
  • "Is there a public dataset for [task/organ/modality]?"
  • "Verify these DOIs/PMIDs." / "Expand my query with MeSH terms."
  • "Build a literature map / gap matrix / local paper RAG plan for this manuscript."

When to open extra files

FileOpen when
references/source-tiers.mdWhich source to query first; fallback order; MeSH; recall vs precision; dedup keys
references/dataset-sources.mdFinding imaging + omics datasets (TCIA, GEO, cBioPortal, OpenNeuro, Grand Challenge, MSD)
references/literature-survey-workflow.mdThe user needs field mapping, Introduction support, reviewer-defense literature, dataset scouting, or a manuscript-level literature synthesis rather than a quick lookup

Workflow

  1. For survey-level or local-corpus work, open literature-survey-workflow.md and choose the mode: Intent / Triage / Deepen / Synthesize / Expand. Use its local paper/RAG ladder before expensive synthesis when a paper database is available.
  2. Classify the need — papers vs datasets; quick lookup vs systematic recall.
  3. Build the query — concepts (translate Chinese → English scientific terms); for PubMed add MeSH; record the strategy for systematic searches.
  4. Search the right sources (source-tiers.md), per-source limits; for data use dataset-sources.md.
  5. Merge & dedup across sources by identifier/title.
  6. Verify key identifiers; flag unresolved.
  7. Return a ranked, deduplicated candidate list (+ a logged strategy for systematic searches). Hand export to radiology-citation.

MCP/tooling note

Works in prompt mode (built-in search tools, following these rules) or with an academic-search MCP exposing search_papers / get_paper_by_id / get_citation / lookup_mesh. Set a contact email for PubMed E-utilities; optionally NCBI_API_KEY for higher rate limits. (Restricted/blocked domains: do not attempt to bypass — report inaccessibility.)

Output contract

  1. Strategy — sources queried, query strings/MeSH, limits, dates (full log for systematic searches).
  2. Candidates — deduplicated: Title | Authors | Year | Venue | DOI/PMID/arXiv | Verified?.
  3. Datasets (if requested) — Name | Source | Modality/omics | n | Access | Accession.
  4. Survey artifacts (if survey-level work) — corpus log, paper-note/gap-matrix summary, local/RAG index status when relevant, exemplar craft notes, reviewer-objection candidates, and claim-ready citation roles.
  5. Unresolved/Incomplete — failed lookups and missing metadata.

Handoffs

Citation grading/export → radiology-citation; dataset access/governance & availability statements → radiology-data; systematic-review reporting → radiology-reporting (PRISMA-DTA); full bilingual read of a key retrieved paper → radiology-reader.

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 Search AI skill do?

Multi-source literature, dataset, and local-corpus search for imaging research, with deduplication, identifier verification, reference management, and survey/RAG workflow support. Searches PubMed, arXiv, Crossref, and imaging dataset registries (TCIA, GEO, cBioPortal, OpenNeuro, Grand Challenge, Medical Segmentation Decathlon), and can structure local paper/RAG workflows when a paper database, PaperQA2, NotebookLM, or paperpipe-style backend exists. Use when the user wants to find papers/datasets, run systematic or survey-level search, verify DOI/PMID/arXiv IDs, map a field, build a gap mat...

Why use Radiology Search on TypingMind?

Because you install it once and use it with any model. Radiology Search 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 Search 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-search. 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 Search?

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 Search?

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

Is the Radiology Search 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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