agentsclimarketplace

Refine literature question

Skill xingtaxueshu/literature-review-skills/skills/refine-literature-question

Five open agent skills for sharper literature reviews: question refinement, review modules, method comparison, contradiction mapping, and research gap discovery.

Install
npx -y skills add xingtaxueshu/literature-review-skills --skill refine-literature-question

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Refine a broad literature review topic, research direction, thesis idea, or fuzzy academic interest into a sharp one-sentence research question. Use when the user is preparing a literature review, thesis proposal, research design, or paper outline and needs to move from a theme like "XX research review" to a concrete "why/how/mechanism" question that can guide literature search and screening.

SKILL.md

2.7 KB, as published. Nobody here has run it

Refine Literature Question

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. Rewrite vague nouns into observable relationships, mechanisms, conditions, or puzzles.
  3. Generate 5-10 alternative question angles rather than one premature answer.
  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:

  • Contain a concrete "why", "how", "through what mechanism", or "under what conditions" focus.
  • 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]?

Output Format

Provide:

## 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: ...

Guardrails

  • Do not accept XX research review, current situation of XX, or development of XX as the final anchor.
  • 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.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.