Refine Literature Question logo

Refine Literature Question

Community
xingtaxueshu
refine-literature-question

Refine a broad literature review topic into a bounded question for searching and screening, including explanatory, scoping, conceptual, and historical reviews. Use for thesis proposals, journalism and communication, or information science review questions. 触发词:综述选题、收紧研究问题、新闻传播综述、情报学综述、开题选题。

Overview

Publisherxingtaxueshu
Repositoryliterature-review-skills
Skill namerefine-literature-question
Stars
78
Forks
6
Bundled files
9
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.

  • 9 bundled files

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

  • Open source

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

Installation

Install the Refine Literature Question 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/xingtaxueshu/literature-review-skills/tree/main/skills/refine-literature-question
  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/xingtaxueshu/literature-review-skills.git /tmp/literature-review-skills
mkdir -p .claude/skills
cp -r /tmp/literature-review-skills/skills/refine-literature-question .claude/skills/refine-literature-question
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Refine Literature Question 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 Refine Literature Question 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 Refine Literature Question 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.

Refine Literature Question

Discipline And Interview Setup

Read discipline-routing.md, then only the selected discipline profile. Reuse the user's existing brief; do not restart intake at each stage. For journalism/communication or information science, use that profile's concepts, extraction fields, and appraisal checks instead of a generic social-science template.

When the user requests "grill me", 追问, or 模拟答辩, read research-interview.md. Ask one decision-relevant question per turn and wait; do not generate the stage deliverable until enough decisions are settled or the user asks to proceed. In quick/no-question mode, record assumptions and continue without inventing answers.

For evidence extraction or drafting, read review-workflow.md. With no sources, give a plan or outline, not a purported synthesis. If reference files are unavailable, retain the source guardrails below and state that discipline guidance is missing.

Goal

Turn a broad topic into a usable question anchor before searching, reading, or writing the review. The output should be a one-sentence research question that forces selection: some papers become clearly relevant, and others become safe to skip.

Workflow

  1. Identify whether the user's input is a topic, a phenomenon, or an actual question.
  2. Identify the review type, then replace vague nouns with bounded concepts, objects, relationships, periods, or puzzles.
  3. Generate 3-5 candidate angles after scope decisions are clear; do not flood an interview turn with candidates.
  4. Evaluate each candidate against the three-anchor test.
  5. Recommend one primary question and 1-2 backups, then explain what each would make the review include or exclude.

Three-Anchor Test

A strong literature review question should:

  • Match the review purpose: explanation, bounded evidence mapping, conceptual distinction, or historical interpretation. A causal "why/how" question is not mandatory.
  • Force literature selection by making some studies irrelevant to the question.
  • Feel worth answering if a reader could get a credible answer from the review.

If the question fails any item, revise it before proceeding to search or outline the literature.

Question Patterns

Use these patterns when generating candidates:

  • Mechanism: Why does [phenomenon] occur in [context], and through what mechanism?
  • Contrast: Why does [outcome] differ between [group/place/time A] and [group/place/time B]?
  • Condition: Under what conditions does [factor] influence [outcome]?
  • Tension: Why do existing studies disagree about [relationship/result]?
  • Process: How does [actor/system] move from [state A] to [state B]?
  • Scoping: What concepts, methods, and populations have been studied for [object] within [scope]?
  • Conceptual/history: How has the meaning of [concept] changed across [traditions/periods], and what does that change explain?

Output Format

Provide:

markdown
## Candidate Questions

1. ...
2. ...

## Recommended Anchor

[One sentence]

## Why This Works

- Selection boundary: ...
- Likely literature modules: ...
- What to skip: ...

## Revision Notes

- If the user wants broader scope: ...
- If the user wants narrower scope: ...

## 待核实清单

[Unverified sources, "未查到" results, sentences marked [需补来源], and inferences that still need checking; write "无" if empty]

## 给作者的问题

[Judgment calls that belong to the author; write "无" if empty]

The output sections above, and the two closing sections, keep fixed names and order; see references/output-contract.md (a copy bundled in this skill folder).

Guardrails

  • Do not accept an unbounded label such as XX research review as the final anchor. Descriptive, historical, and conceptual reviews are valid when their corpus, scope, and analytical purpose are explicit.
  • Do not invent a domain-specific claim when the user has not provided enough context; offer candidate angles and mark assumptions.
  • Treat AI as a sparring partner: generate options and pressure-test wording, but keep the user's academic judgment in charge.
  • Source integrity: follow references/source-integrity.md. Claims about what "existing studies" do or disagree on are assumptions until the user supplies sources; mark them. Use only sources the user provided or that are verified, tag each with a status (用户提供 / 已核验 / 未核验), write "未查到" instead of completing authors, year, journal, or pages, and put anything unverified in ## 待核实清单. If that file is not available, still apply these rules.

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 Refine Literature Question AI skill do?

Refine a broad literature review topic into a bounded question for searching and screening, including explanatory, scoping, conceptual, and historical reviews. Use for thesis proposals, journalism and communication, or information science review questions. 触发词:综述选题、收紧研究问题、新闻传播综述、情报学综述、开题选题。

Why use Refine Literature Question on TypingMind?

Because you install it once and use it with any model. Refine Literature Question 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 Refine Literature Question in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xingtaxueshu/literature-review-skills/tree/main/skills/refine-literature-question. 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 Refine Literature Question?

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 Refine Literature Question?

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

Is the Refine Literature Question AI skill free?

Yes. It is published on GitHub by xingtaxueshu 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 👇