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research-question-refiner研究问题细化器

Agent Skill

research-question-refiner 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

346

周安装

14

GitHub Stars

3

下载量

109
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:research-question-refiner(研究问题细化器)
来源仓库:https://github.com/ghostscientist/skills
仓库路径:skills/research-question-refiner
安装命令:
npx skills add https://github.com/ghostscientist/skills --skill research-question-refiner
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/ghostscientist/skills --skill research-question-refiner

简介

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 适用于需要根据关键词或任务场景进行信息检索的研究类 Agent 工作流。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装使用。
  • 建议确认权限范围和维护状态,注意可能触发联网或文件读写操作。
  • research-question-refiner 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Research Question Refiner

Transform "I'm interested in X" into "I will investigate whether Y under conditions Z, measuring W."

The Problem

Most research ideas fail not because they're bad, but because they're:

  • Too vague to act on
  • Too ambitious to complete
  • Too incremental to matter
  • Missing a clear success criterion

This skill fixes that.

Process

Stage 1: Excavate the Interest

Start by understanding what's actually pulling at you:

Questions to ask:

  1. What sparked this interest? (Paper, conversation, problem you encountered?)
  2. What's the version that excites you most?
  3. What would be cool if it worked?
  4. Who would care about the answer?

Output: A paragraph capturing the raw interest, unfiltered.

Stage 2: Map the Territory

Before scoping, understand the landscape:

What's Known:

  • What's the current state-of-the-art?
  • What are the established approaches?
  • What have people tried that didn't work?

What's Unknown:

  • What are the acknowledged open problems?
  • What assumptions does current work make?
  • Where do methods fail?

What's Controversial:

  • Where do researchers disagree?
  • What's claimed but not convincingly shown?
  • What's believed but not rigorously tested?

Output: A structured map with citations/references for each area.

Stage 3: Find the Gap

A good research question lives in a gap that is:

PropertyToo LittleJust RightToo Much
NoveltyRedoing existing workNew angle or combinationNo foundation to build on
DifficultyTrivial to answerChallenging but doableRequires breakthroughs
ImpactNo one caresCommunity would update beliefsNobel prize (unrealistic)
ScopeOne experimentThesis chapter / paperMultiple PhDs

Gap-finding questions:

  • What would change if we relaxed assumption X?
  • What if we applied method A to domain B?
  • What's between approach X and approach Y?
  • What fails in setting Z that works elsewhere?

Output: 3-5 candidate gaps, each as one sentence.

Stage 4: Refine to Concrete Question

For each candidate gap, sharpen into a question:

The Formula:

[Action verb] + [specific phenomenon] + [under conditions] + [measurable outcome]

Examples of refinement:

❌ Vague: "How can we make transformers more efficient?" ✅ Concrete: "Does structured sparsity in attention patterns preserve performance on long-context tasks while reducing compute by >50%?"

❌ Vague: "Can robots learn from humans better?" ✅ Concrete: "Does incorporating gaze direction in demonstrations improve sample efficiency for manipulation tasks compared to kinesthetic teaching alone?"

❌ Vague: "What makes language models hallucinate?" ✅ Concrete: "Do retrieval-augmented models hallucinate less on factual questions when retrieval confidence is used to modulate generation temperature?"

Stage 5: Feasibility Check

For each refined question, assess:

Resources Required:

  • Compute: GPU-hours estimate
  • Data: Available or needs collection?
  • Time: Weeks/months realistically
  • Expertise: What skills are needed?

Risk Assessment:

  • What's the probability this works at all?
  • What if the hypothesis is wrong? (Is negative result publishable?)
  • What could go wrong technically?
  • What could invalidate the whole direction?

Dependencies:

  • Does this require other work to finish first?
  • Are there rate-limiting steps?
  • What can be parallelized?

Stage 6: The Litmus Tests

A good research question passes all of these:

The Advisor Test:

"If I pitched this in 2 minutes, would a busy professor say 'yes, go do that' rather than 'hmm, let's talk more'?"

The Paper Test:

"Can I envision the title, abstract, and figure 1 of the resulting paper?"

The Null Result Test:

"If my hypothesis is wrong, would that still be interesting to report?"

The Motivation Test:

"Am I actually excited to work on this for 6+ months?"

The Explanation Test:

"Can I explain why this matters to a smart non-expert in 60 seconds?"

Output Format

Deliver a Research Question Brief:

# Research Question Brief

## The Interest (Raw)
[Original unfiltered interest]

## Territory Map

### What's Known
- [Point 1] ([citation])
- [Point 2] ([citation])

### What's Unknown
- [Open question 1]
- [Open question 2]

### What's Controversial
- [Debate 1]

## Candidate Gaps
1. [Gap 1]
2. [Gap 2]
3. [Gap 3]

## Refined Questions

### Question 1: [Title]
**Statement:** [Precise question]
**Hypothesis:** [What you expect to find]
**Feasibility:** [Brief assessment]
**If it works:** [Impact]
**If it doesn't:** [What we still learn]

### Question 2: [Title]
[Same structure]

## Recommendation
[Which question to pursue and why]

## Immediate Next Steps
1. [Concrete action 1]
2. [Concrete action 2]
3. [Concrete action 3]

Common Failure Modes

The Kitchen Sink: Trying to answer too many questions at once → Fix: Ruthlessly cut until there's ONE core question

The Solution in Search of a Problem: Starting with a method, not a question → Fix: Ask "Who has this problem? Why hasn't it been solved?"

The Incremental Trap: Small delta on existing work → Fix: Ask "Would this change how people think?"

The Impossible Dream: Beautiful question, can't be answered → Fix: Ask "What's the minimal version that's still interesting?"

The Boring Sure Thing: Will definitely work, nobody cares → Fix: Add ambition until there's meaningful risk

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.93%
按下载量换算39

Claude

32%
按下载量换算35

Cursor

18.34%
按下载量换算20

Gemini CLI

10.74%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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