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VS-Enhanced Research Question Refiner - Prevents Mode Collapse and derives differentiated research questions Enhanced VS 3-Phase process: Modal question avoidance, alternatives presentation, differentiated RQ recommendation Use when: refining research ideas, formulating research

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auto-empirical-research-skills
3.3k200 skills146 agents
Install
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill a1 --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/a1

Context preview

The summary Claude sees to decide when to auto-load this skill.

VS-Enhanced Research Question Refiner - Prevents Mode Collapse and derives differentiated research questions Enhanced VS 3-Phase process: Modal question avoidance, alternatives presentation, differentiated RQ recommendation Use when: refining research ideas, formulating research

SKILL.md

a1.SKILL.md
name: a1
description: |
  VS-Enhanced Research Question Refiner - Prevents Mode Collapse and derives differentiated research questions
  Enhanced VS 3-Phase process: Modal question avoidance, alternatives presentation, differentiated RQ recommendation
  Use when: refining research ideas, formulating research questions, clarifying scope
  Triggers: research question, 연구 질문, PICO, SPIDER, research idea
version: "12.0.1"

⛔ Prerequisites (v8.2 — MCP Enforcement)

Entry point agent — no prerequisites required.

Checkpoints During Execution

  • 🔴 CP_RESEARCH_DIRECTION → `diverga_mark_checkpoint("CP_RESEARCH_DIRECTION", decision, rationale)`
  • 🔴 CP_VS_001 → `diverga_mark_checkpoint("CP_VS_001", decision, rationale)`
  • 🔴 CP_VS_003 → `diverga_mark_checkpoint("CP_VS_003", decision, rationale)`

Fallback (MCP unavailable)

Read `.research/decision-log.yaml` directly to verify prerequisites. Conversation history is last resort.

---

Research Question Refiner

**Agent ID**: 01 **Category**: A - Theory & Design **VS Level**: Enhanced (3-Phase) **Tier**: Core **Icon**: 🎯

Overview

Transforms vague research ideas into clear, testable research questions. Systematically structures research questions using PICO/SPIDER frameworks.

Applies **VS-Research methodology** to avoid overly broad or predictable research questions, deriving differentiated questions with clear academic contribution.

VS-Research 3-Phase Process (Enhanced)

Phase 1: Modal Research Question Identification

**Purpose**: Explicitly identify the most predictable "obvious" research questions

⚠️ **Modal Warning**: The following are the most predictable research questions for [topic]:

| Modal Research Question | T-Score | Problem |
|------------------------|---------|---------|
| "Effect of [X] on [Y]" | 0.90 | Scope too broad, no differentiation |
| "Relationship between [X] and [Y]" | 0.85 | Lacks specificity |
| "Analysis of [X] effects" | 0.88 | Mediating variables unclear |

➡️ This is the baseline. We will explore more specific and differentiated questions.

Phase 2: Alternative Research Questions

**Purpose**: Present differentiated research questions in 3 directions based on T-Score

**Direction A** (T ≈ 0.7): Safe but specific
- [Add specific context, specify moderators]
- Example: "Effect of AI feedback on writing accuracy of novice English learners in online learning environments"

**Direction B** (T ≈ 0.4): Differentiated angle
- [Explore new mediation pathways, boundary conditions]
- Example: "Indirect effect of AI feedback immediacy on writing self-efficacy through learner metacognitive regulation"

**Direction C** (T < 0.3): Innovative approach
- [Challenge existing assumptions, reverse causality, non-linear relationships]
- Example: "Paradoxical effects of emotional responses to AI feedback on learning persistence: Negative impact of positive feedback"

Phase 4: Recommendation Execution

For **selected research question**: 1. PICO(S)/SPIDER structuring 2. Operational definition of variables 3. Feasibility assessment 4. Specify theoretical contribution points

---

Research Question Typicality Score Reference

T > 0.8 (Modal - Avoid):
├── "What is the effect of [X] on [Y]?" (Simple causation)
├── "What is the relationship between [X] and [Y]?" (Simple correlation)
├── "Survey on perceptions of [X]" (Descriptive)
└── "Current status and improvement of [X]" (Practitioner report)

T 0.5-0.8 (Established - Needs specificity):
├── Add moderators (when, under what conditions)
├── Add mediators (why, through what mechanism)
├── Specify target/context (for whom, where)
└── Specify comparison groups (compared to what)

T 0.3-0.5 (Emerging - Recommended):
├── Explore multiple mediation pathways
├── Moderated mediation models
├── Explore boundary conditions
└── Temporal dynamics (when effects appear and disappear)

T < 0.3 (Innovative - For top-tier):
├── Challenge existing assumptions
├── Explore reverse causality
├── Non-linear/paradoxical relationships
└── Name new phenomena

When to Use

  • When you have a research topic but no specific question
  • When research question scope needs adjustment (too broad or narrow)
  • When assessing research feasibility
  • When determining descriptive/explanatory/exploratory question types

Core Features

1. **PICO(S) Framework Application**

  • Population (Target population)
  • Intervention/Exposure (Intervention/Exposure)
  • Comparison (Comparison group)
  • Outcome (Outcome variables)
  • Study design (Research design)

2. **SPIDER Framework** (For qualitative research)

  • Sample
  • Phenomenon of Interest
  • Design
  • Evaluation
  • Research type

3. **Question Type Classification**

  • Descriptive: Characterizing phenomena
  • Explanatory: Establishing causality
  • Exploratory: Exploring new areas

4. **Feasibility Assessment**

  • Measurability
  • Resources (time, budget, personnel)
  • Ethical constraints
  • Data accessibility

Input Requirements

Required:
  - initial_research_idea: "Research topic or phenomenon of interest"

Optional:
  - field: "Education, Psychology, Business, etc."
  - available_resources: "Time, budget, accessible data"
  - constraints: "Ethical or practical limitations"

Output Format (VS-Enhanced)

## Research Question Analysis Results (VS-Enhanced)

---

### Phase 1: Modal Research Question Identification

⚠️ **Modal Warning**: The following are the most predictable questions for [topic]:

| Modal Question | T-Score | Problem |
|---------------|---------|---------|
| [Question 1] | 0.90 | [Problem] |
| [Question 2] | 0.85 | [Problem] |

➡️ This is the baseline. We will explore more specific questions.

---

### Phase 2: Alternative Research Questions (T-Score based)

**Direction A** (T = 0.65): Specific question
- RQ: "[Question with specific context]"
- Advantages: Easier peer review defense, clear scope
- Suitable for: Firs
Read more
Ships withauto-empirical-research-skills

📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md(扩展正文,总表行内的 → 直接跳转到对应锚点)。 English version: README-en.md · 中文扩展正文:docs/CONTENT_ZH.md · README-zh-CN.md 已弃用(重定向占位) 🌐 语言: English |

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