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Radiology Bibliometrics

CommunityPopular
huang-sir1
radiology-bibliometrics

Build reproducible radiology bibliometrics with corpus identity, disambiguation and sensitivity; not reviews.

Overview

Publisherhuang-sir1
Repositoryradiology-skills
Skill nameradiology-bibliometrics
Stars
2K
Forks
20
Bundled files
8
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.

  • 8 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 Bibliometrics 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-bibliometrics
  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-bibliometrics .claude/skills/radiology-bibliometrics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Radiology Bibliometrics 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 Bibliometrics 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 Bibliometrics 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 Bibliometrics

Use this skill to describe and map a frozen scholarly corpus: publication/citation patterns, collaboration, topical structure and network change. It owns the bibliometric analysis contract and responsible interpretation. It does not determine whether an intervention works, whether evidence is trustworthy, or whether a topic is scientifically innovative.

Non-negotiable boundaries

  • Corpus before metrics. Consume a versioned corpus passport containing database/index coverage, exact query, filters, document types, languages, retrieval date, export/API route and deduplication state. radiology-search supplies or freezes the scientific corpus.
  • Coverage is part of the result. Web of Science, Scopus, PubMed, OpenAlex, Crossref and regional indexes cover different works, references and document types. Counts from one source are not a census of scholarship and should not be silently merged with another.
  • Identity is evidence, not a name string. Use authenticated/persistent identifiers where available, then affiliation, coauthor, topic and time evidence. Preserve unresolved author and institution clusters; name-only matching is insufficient.
  • Counting choices change claims. Declare full or fractional counting, the fractional unit, denominator, treatment of multi-affiliation and hyperauthorship, and sensitivity to alternatives.
  • Normalize before comparison. Citation comparisons require an explicit field/classification, document type, publication year and citation window. Recent work is right-censored.
  • Networks are model outputs. Edge definition, normalization, thresholds, pruning, clustering algorithm, resolution, seed and label rules must be recorded. A colored community is not a natural scientific truth; test threshold and community stability.
  • Metrics are not merit. Citation, centrality, journal metrics and altmetrics indicate forms of attention or network position; they do not establish quality, causal impact, clinical benefit or innovation. Quantitative indicators support, not replace, expert and qualitative judgment.
  • No evidence-synthesis laundering. Co-citation, keyword frequency or publication volume cannot answer a diagnostic, prognostic or treatment-effect question.

Input passport and STOP gates

Start with corpus-passport.md. Required fields include:

  • purpose, unit of assessment, date range and intended comparison;
  • database/platform, indexes, coverage limits, query and retrieval timestamp;
  • export fields, cited-reference availability, API/snapshot/version and access/license constraints;
  • inclusion/exclusion, document types, preprints/proceedings, language and correction/retraction rules;
  • DOI and non-DOI deduplication; work/version/family policy;
  • author/institution identifiers, disambiguation evidence and unresolved cases;
  • metric definitions, counting method, normalization, citation window and network specification.

Return a STOP state when:

  • STOP_CORPUS: query, database coverage, retrieval date or inclusion logic is missing;
  • STOP_IDENTITY: unresolved author/institution identity materially changes ranks or networks;
  • STOP_METRIC: a requested indicator lacks a denominator, window or reproducible definition;
  • STOP_NETWORK: the network is dominated by arbitrary thresholds, disconnected fragments or unstable communities without sensitivity analysis;
  • STOP_INTERPRETATION: the requested conclusion equates metrics with quality, clinical impact or innovation;
  • STOP_ACCESS: data reuse, API terms or export rights do not support the proposed analysis.

A STOP may still permit a diagnostic audit and repair plan; it blocks definitive comparative claims.

Route by mode

NeedModeMain contract
Check search/export fitnesscorpus-auditcoverage, query, retrieval, fields, DOI/version dedup and missingness
Describe output and attentiondescriptive-impacttime series, document/citation distributions and normalized indicators
Map coauthorship or institutionscollaboration-networkidentity resolution, full/fractional counting, edge and consortium policy
Co-word, co-citation or coupling mapscience-mappingfeature construction, normalization, thresholds, clustering and labels
Compare authors/institutions/countriesbenchmarkingfair comparison set, counting, normalization, uncertainty and responsible metrics
Examine longitudinal topic/network changetemporal-evolutionfixed/moving windows, database drift, label continuity and stability
Reproduce or challenge a prior mapaudit-reanalysisfrozen inputs, method replay, sensitivity matrix and claim-drift ledger

For methods read analysis-contract-and-methods.md. For claim limits read responsible-interpretation.md. Use the source registry for source/version status.

Radiology-specific controls

  • Search across radiology, nuclear medicine, medical physics, imaging informatics, engineering and computer-science venues when the question crosses them; report conference/preprint asymmetry.
  • Preserve modality, anatomy, task, clinical purpose, reference-standard and development/validation/ deployment state. Keyword presence alone cannot reliably assign clinical maturity.
  • Disambiguate hospitals, university systems, cancer centers and consortia; report whether networks count authors, addresses, organizations or countries.
  • Treat multi-center imaging consortia and hyperauthored AI papers explicitly; a consortium edge can dominate maps without representing ordinary collaboration.
  • Vendor names, scanner families and software ecosystems may be affiliation/technology entities, not scholarly authors; do not mix entity types.
  • High publication volume in imaging AI does not prove clinical uptake, regulatory authorization or improved patient outcomes.

Workflow and output contract

  1. Freeze the corpus passport and an immutable record-ID/DOI list with exclusions and reasons.
  2. Resolve work versions and identities; keep a disambiguation ledger and manual-review queue.
  3. Define every metric, denominator, count, normalization and time window before computing it.
  4. Define networks completely, run threshold/resolution/seed and counting sensitivities, and assess community/centrality stability rather than presenting a single attractive map.
  5. Build bibliometric-analysis-and-claim-ledger.md.
  6. Return: Corpus flow, Coverage and missingness, Identity ledger, Metric dictionary, Network specification, Sensitivity/stability results, Claim-source ledger, Limitations, and Reproducibility manifest.

Label conclusions as DESCRIPTIVE, COMPARATIVE, NETWORK_STRUCTURAL, HYPOTHESIS_GENERATING or NOT_SUPPORTED. Never promote them to evidence-effect, quality or innovation conclusions.

Handoffs and non-ownership

  • Corpus retrieval/search reproducibility → radiology-search.
  • Novel question and qualitative opportunity assessment → radiology-frontier.
  • Intervention/diagnostic/prognostic evidence conclusions and risk-of-bias appraisal → radiology-systematic-review.
  • Verification of an individual citation or reference metadata → radiology-citation.
  • Statistical uncertainty methods beyond the bibliometric contract → radiology-stats.
  • Figures and tables for publication → radiology-figure / radiology-table.
  • This skill does not rank people for hiring/funding, infer research quality from journal metrics, or make causal claims about collaboration, policy or clinical impact.

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 Bibliometrics AI skill do?

Build reproducible radiology bibliometrics with corpus identity, disambiguation and sensitivity; not reviews.

Why use Radiology Bibliometrics on TypingMind?

Because you install it once and use it with any model. Radiology Bibliometrics 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 Bibliometrics 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-bibliometrics. 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 Bibliometrics?

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 Bibliometrics?

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

Is the Radiology Bibliometrics 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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