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Question Refiner

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cnfjlhj
question-refiner

Use when a research question is still vague and must be clarified into a structured deep-research brief before actual literature research or execution. Skip this if the user already has a concrete paper draft or a ready-to-run research specification.

Overview

Publishercnfjlhj
Repositoryai-collab-playbook
Skill namequestion-refiner
Stars
455
Forks
44
Bundled files
2
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 cnfjlhj on GitHub. Read the source before you install it.

Installation

Install the Question Refiner 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/cnfjlhj/ai-collab-playbook/tree/main/skills/full/question-refiner
  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/cnfjlhj/ai-collab-playbook.git /tmp/ai-collab-playbook
mkdir -p .claude/skills
cp -r /tmp/ai-collab-playbook/skills/full/question-refiner .claude/skills/question-refiner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Question Refiner 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 Question Refiner 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 Question Refiner 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.

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:

markdown
### 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.

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 Question Refiner AI skill do?

Use when a research question is still vague and must be clarified into a structured deep-research brief before actual literature research or execution. Skip this if the user already has a concrete paper draft or a ready-to-run research specification.

Why use Question Refiner on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cnfjlhj/ai-collab-playbook/tree/main/skills/full/question-refiner. 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 Question Refiner?

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 Question Refiner?

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

Is the Question Refiner AI skill free?

It is published on GitHub by cnfjlhj. Check the repository for licensing terms. 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.

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