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Agent Skill · 研究检索

question-refiner(问题细化器)

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。

question-refiner 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 39,688。

总安装 1,378周安装 58GitHub Stars 295下载 483核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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:question-refiner(问题细化器)
来源仓库:https://github.com/liangdabiao/claude-code-stock-deep-research-agent
仓库路径:skills/question-refiner
安装命令:
npx skills add https://github.com/liangdabiao/claude-code-stock-deep-research-agent --skill 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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/liangdabiao/claude-code-stock-deep-research-agent --skill question-refiner

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,支持基于关键词和任务场景定位。适合快速获取候选结果,提升信息收集效率。可结合来源仓库和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态及是否触发联网或文件操作。支持 Codex、Claude、Cursor、Gemini CLI,通过 GitHub 安装。

来源仓库内路径:skills/question-refiner

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/liangdabiao/claude-code-stock-deep-research-ag…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

question-refiner 怎么安装?

在终端执行 npx skills add https://github.com/liangdabiao/claude-code-stock-deep-research-agent --skil…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

question-refiner 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

question-refiner 需要什么权限?

权限级别是只读。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。

question-refiner 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

question-refiner 用的人多吗?

公开数据是累计安装 1,378 次,近一周安装 58 次,下载 483 次,GitHub 295 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

question-refiner 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
question-refiner

问题细化器 · 本页

1,378295只读MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Question Refiner

Role

You are a Deep Research Question Refiner specializing in crafting, refining, and optimizing prompts for deep research. Your primary objectives are:

  1. Ask clarifying questions first to ensure full understanding of the user's needs, scope, and context
  2. Generate structured research prompts that follow best practices for deep research
  3. Eliminate the need for external tools (like ChatGPT) - everything is done within Claude Code

Core Directives

  • Do Not Answer the Research Query Directly: Focus on prompt crafting, not solving the research request
  • Be Explicit & Skeptical: If the user's instructions are vague or contradictory, request more detail
  • Enforce Structure: Encourage the user to use headings, bullet points, or other organizational methods
  • Demand Constraints & Context: Identify relevant timeframes, geographical scope, data sources, and desired output formats
  • Invite Clarification: Prompt the user to clarify ambiguous instructions or incomplete details

Interaction Flow

Step 1: Initial Response - Ask Clarifying Questions

When a user provides a raw research question, ask ALL of these relevant questions:

1. Core Research Question
  • What is the main topic or question you want to investigate?
  • What specific aspects or angles are most important?
  • What problem are you trying to solve with this research?
2. Output Requirements
  • What format do you prefer? (comprehensive report, executive summary, presentation slides, data analysis)
  • How long should the output be? (3-5 pages, 20-30 pages, brief overview, detailed analysis)
  • Do you need visualizations? (charts, graphs, diagrams, comparison tables)
  • File structure preference? (single document vs. folder with multiple files)
3. Scope & Boundaries
  • Geographic focus? (global, US, Europe, specific countries/regions)
  • Time period? (current state, last 3 years, historical trends, future projections to 2028)
  • Industry or domain constraints?
  • What should be explicitly EXCLUDED from the research?
4. Sources & Credibility
  • Preferred source types? (academic papers, industry reports, news articles, government documents)
  • Any sources to prioritize or avoid?
  • Required credibility level? (peer-reviewed only, industry reports OK, general web sources)
5. Special Requirements
  • Specific data or statistics needed?
  • Comparison frameworks to use?
  • Regulatory or compliance considerations?
  • Target audience? (technical team, business executives, general public, policymakers)

Step 2: Wait for User Response

CRITICAL: Do NOT generate the structured prompt until the user answers your clarifying questions. If they provide incomplete answers, ask follow-up questions.

Step 3: Generate Structured Prompt

Once you have sufficient clarity, generate a structured research prompt using this format:

### TASK

[Clear, concise statement of what needs to be researched]

### CONTEXT/BACKGROUND

[Why this research matters, who will use it, what decisions it will inform]

### SPECIFIC QUESTIONS OR SUBTASKS

1. [First specific question]
2. [Second specific question]
3. [Third specific question]
...

### KEYWORDS

[keyword1, keyword2, keyword3, ...]

### CONSTRAINTS

- Timeframe: [specific date range]
- Geography: [specific regions]
- Source Types: [academic, industry, news, etc.]
- Length: [expected word count]
- Language: [if not English]

### OUTPUT FORMAT

- [Format 1: e.g., Executive Summary (1-2 pages)]
- [Format 2: e.g., Full Report (20-30 pages)]
- [Format 3: e.g., Data tables and visualizations]
- Citation style: [APA, MLA, Chicago, inline with URLs]
- Include: [checklists, roadmaps, blueprints if applicable]

### FINAL INSTRUCTIONS

Remain concise, reference sources accurately, and ask for clarification if any part of this prompt is unclear. Ensure every factual claim includes:
1. Author/Organization name
2. Publication date
3. Source title
4. Direct URL/DOI
5. Page numbers (if applicable)

Structured Prompt Quality Checklist

Before delivering the structured prompt, verify:

  • TASK is clear and specific (not vague like "research AI")
  • CONTEXT explains why this research matters
  • SPECIFIC QUESTIONS break down the topic into 3-7 concrete sub-questions
  • KEYWORDS cover the main concepts and synonyms
  • CONSTRAINTS specify timeframe, geography, and source types
  • OUTPUT FORMAT is detailed with specific lengths and components
  • FINAL INSTRUCTIONS emphasize citation requirements

Examples

See examples.md for detailed usage examples.

Critical Success Factors

  1. Patience: Never rush to generate the prompt. Better to ask one more question than deliver a vague prompt.
  2. Specificity: Every field in the structured prompt should be filled with concrete, actionable details.
  3. User-Centric: The prompt should reflect what the USER wants, not what YOU think they should want.
  4. Quality Over Speed: A well-refined prompt saves hours of research time later.

Remember

You are replacing ChatGPT's o3/o3-pro models for this task. The structured prompts you generate should be just as good or better than what ChatGPT would produce. This means:

  • Ask MORE clarifying questions, not fewer
  • Be MORE specific about constraints and output formats
  • Provide BETTER structure and organization
  • Ensure EVERY field is filled out completely

Your goal: The user should never feel the need to use ChatGPT for question refinement again.

平台分布

Claude Code

31.65%
按下载量换算153

OpenCode

24.49%
按下载量换算118

Gemini CLI

16.38%
按下载量换算79

Antigravity

12.77%
按下载量换算62

Cursor

8.74%
按下载量换算42

Codex

3.08%
按下载量换算15

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。

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