Q Litreview logo

Q Litreview

Community
TyrealQ
q-litreview

Draft or refine standalone literature review sections for academic manuscripts. Builds progressive arguments from theory through prior research to the study's approach, with research questions earned through the argument. Use for literature reviews, theoretical framing, or research question development.

Overview

PublisherTyrealQ
Repositoryq-skills
Skill nameq-litreview
Stars
109
Forks
1
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Q Litreview 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/TyrealQ/q-skills/tree/main/skills/q-scholar/q-litreview
  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/TyrealQ/q-skills.git /tmp/q-skills
mkdir -p .claude/skills
cp -r /tmp/q-skills/skills/q-scholar/q-litreview .claude/skills/q-litreview
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Q Litreview 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 Q Litreview 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 Q Litreview 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.

Q-Litreview

Draft standalone literature review sections that follow the introduction. Produces a progressive argument across two subsections, arriving at research questions as the earned conclusion.

References

  • references/literature_review_template.md — structural guidance (draft dynamically, not verbatim)
  • references/interview_questions.md — interview protocol and refinement diagnostic
  • ../references/apa_style_guide.md — APA formatting, numbers, notation, formulas

Core Principles

  • Purely conceptual and theoretical; measurement methodology belongs in methods
  • Progressive argument: theory exposition, prior research, limitations, new approach, earned RQs
  • Narrative prose; no bullet points, em-dashes, or italicized sublabels within paragraphs
  • Prefer 3-12 sentence paragraphs; no standalone introductory paragraphs
  • Dense citation support with specific findings woven into prose, not cataloged
  • Two subsections with descriptive headers naming the dimension or activity
  • Each paragraph has one clear through-line advancing the argument
  • Research questions emerge as the logical conclusion, not restatements of the introduction

Argumentative Architecture

The templates describe what goes where; this section describes why each element appears where it does.

Subsection 1 (Theoretical Framework and Domain Application)

  • P1: Establish the framework (core constructs, key evidence, mechanisms)
  • P2: Show it applies beyond its original context (digital, mediated, computational extensions)
  • P3: Show the study's domain needs it (what existing frameworks capture and miss)
  • P4: Show where it has not yet been applied (the specific understudied context)

Each paragraph opens by extending or complicating the prior paragraph's conclusion.

Subsection 2 (Prior Research Tradition to New Approach)

  • P5: What the field knows and how (foundational studies, methodological pattern)
  • P6: How the environment has changed (platform dynamics, new metrics)
  • P7: Why existing tools cannot capture the new reality (limitations)
  • P8: The approach that can (content-side framing, earned RQs)

Subsection Headers

Use descriptive headers: "[Theory] in [Domain]: [Construct 1] and [Construct 2] in [Context]" and "[Field]-Mediated [Activity]: From [Existing] to [New Approach]."

Research Question Framing

RQs should feel earned by the argument, connected to the preceding analysis rather than restating the introduction's wording.

Workflow

StepActionReference
1Interview: if an introduction exists, read it first to identify literature it previews. Ask what literature review length is typical for the target venue, and scale the eight-paragraph structure to fit.references/interview_questions.md
2Outline: map progressive argument across 8 paragraphs (4 per subsection)Architecture above
3Draft: write flowing paragraphs of 6-10 sentencesreferences/literature_review_template.md
4Coordination (if intro exists): verify intro compresses, lit review elaborates; theory defined here; concrete examples here; coined phrases in one section only; RQ framing differs—
5Refinement (existing drafts): run diagnostic, then revise per Architecturereferences/interview_questions.md

Scope

Include: Theoretical framework (origins, dimensions, mechanisms, extensions), synthesis of prior research with specific contributions, gaps as narrative consequences, conceptual motivation for the analytical approach, earned research questions.

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 Q Litreview AI skill do?

Draft or refine standalone literature review sections for academic manuscripts. Builds progressive arguments from theory through prior research to the study's approach, with research questions earned through the argument. Use for literature reviews, theoretical framing, or research question development.

Why use Q Litreview on TypingMind?

Because you install it once and use it with any model. Q Litreview 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 Q Litreview in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TyrealQ/q-skills/tree/main/skills/q-scholar/q-litreview. 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 Q Litreview?

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 Q Litreview?

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

Is the Q Litreview AI skill free?

Yes. It is published on GitHub by TyrealQ 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇