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Review Paper

Organization
Aperivue
review-paper

Use when writing a literature review article (narrative, scoping PRISMA-ScR or systematic). Scaffolds a 7-part skeleton with a scope and non-overlap statement, summary-table stubs and reporting-guideline wiring, without inventing citations. Original research is /write-paper.

Overview

PublisherAperivue
Repositorymedsci-skills
Skill namereview-paper
Stars
349
Forks
82
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Aperivue on GitHub. Read the source before you install it.

Installation

Install the Review Paper 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/Aperivue/medsci-skills/tree/main/skills/review-paper
  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/Aperivue/medsci-skills.git /tmp/medsci-skills
mkdir -p .claude/skills
cp -r /tmp/medsci-skills/skills/review-paper .claude/skills/review-paper
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Paper 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 Review Paper 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 Review Paper 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.

Review-Paper Skill

The review-article counterpart to write-paper (original research). For reviewing someone else's review article, use /peer-review or /self-review (the RV1-RV9 probes) instead.

Step 0 — Format + spine axis (the structure-determining choice)

  1. Confirm format with the user: narrative (SANRA) | scoping (PRISMA-ScR + JBI) | systematic (PRISMA 2020). This decides the reporting guideline and the registration path.
  2. Choose the spine axis — the single most consequential decision: organize the body by modality (e.g. 2D → 3D), by task (generation / QA / deployment), or by lifecycle stage. Every body section then follows this one axis; mixing axes is the most common structural failure.
  3. Require a scope statement + non-overlap boundary against prior/adjacent reviews — this pre-empts the reviewer's first question, "why another review on this?" (user-approval checkpoint: confirm the boundary with the user before scaffolding).

Step 1 — Scaffold the macro skeleton for the format

Load ${CLAUDE_SKILL_DIR}/references/macro_skeleton.md and write the section for the chosen format to manuscript.md in the manuscript directory, with the summary-table stubs (Step 2) and that section's figure/table legend plan. The structure depends on the format:

  • Narrative — the 7-part skeleton below.
  • Scoping / systematic — IMRaD with one Methods and one Results slot per PRISMA-ScR or PRISMA 2020 item (the reference lists them by item number). Do not use the 7-part skeleton for these: it has no place for eligibility criteria, search, selection, charting/extraction, risk of bias or synthesis methods, nor for study-selection results. The thematic body by spine axis goes inside Results → synthesis; frontiers and the evaluation-metrics critique go inside Discussion.

The narrative 7-part skeleton:

  1. Abstract — a 4-5 move version (structured for scoping/systematic).
  2. Introduction — clinical motivation → technology → scope + non-overlap block (required field, all formats) → "this review…".
  3. Background / technical principles — tight; cite once, do not re-survey the field.
  4. Thematic body by spine axis — each section ends with a summary table (stub generated to match the type, Step 2).
  5. Frontiers / what is advancing.
  6. Challenges / discussion — include an evaluation-metrics critique subsection (a required quality signal: how the field measures itself, and where those metrics mislead).
  7. Conclusion — measured. A scoping review maps the evidence; it issues no recommendation-grade language or clinical recommendations.

Every citekey must resolve to the project's verified _src/refs.bib (produced by /search-lit → /lit-sync → /verify-refs). If a claim needs a reference not yet in the library, leave a [@NEW:short-topic] placeholder and route it to /search-lit; never fabricate a DOI, author, year, or citekey.

Step 2 — Summary-table stub (matched to type)

  • Narrative / scoping: study | year | [spine-axis value] | method | key finding.
  • Systematic: PRISMA flow + a study-characteristics table + an extraction table.

The stub ships with column headers and one placeholder row. Cells (study, year, metric, finding) are filled only from sources the user supplies or that are verified; an unknown cell stays a placeholder.

Step 3 — Reporting + registration wiring

  • Scoping → PRISMA-ScR (+ JBI charting) + OSF registration.
  • Narrative → SANRA (a 6-item appraisal aid, not a reporting checklist — do not over-enforce it).
  • Systematic → PRISMA 2020 (+ PROSPERO registration).
  • If /check-reporting does not carry the chosen checklist (it bundles PRISMA-ScR and PRISMA 2020, not SANRA), track a manual gap table and flag it for the user rather than silently skipping the item.

Step 4 — QC hand-off

Run the standard manuscript QC chain, which this skill is designed to feed:

  1. /self-review — the RV1-RV9 narrative-review probes auto-activate for a review article.
  2. /check-reporting — PRISMA-ScR or PRISMA 2020 (SANRA via the Step 3 manual table).
  3. /verify-refs — every citation resolves; 0 FABRICATED / MISMATCH.
  4. /humanize — AI-pattern density below threshold.
  5. /academic-aio — discoverability pass (optional).

Convergence gate — the draft is not done until: self-review fatal = 0; verify-refs FABRICATED/MISMATCH = 0; no [@NEW:...] placeholder citations remain; humanize density < 2.0.

Guards

  • Proportionate self-citation; declare an intellectual conflict of interest when an author has contributed to the area being reviewed.
  • Write only inside the manuscript directory.

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 Review Paper AI skill do?

Use when writing a literature review article (narrative, scoping PRISMA-ScR or systematic). Scaffolds a 7-part skeleton with a scope and non-overlap statement, summary-table stubs and reporting-guideline wiring, without inventing citations. Original research is /write-paper.

Why use Review Paper on TypingMind?

Because you install it once and use it with any model. Review Paper 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 Review Paper in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Aperivue/medsci-skills/tree/main/skills/review-paper. 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 Review Paper?

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 Review Paper?

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

Is the Review Paper AI skill free?

Yes. It is published on GitHub by Aperivue 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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