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VS-Enhanced Theoretical Framework Architect with Critique & Visualization Full VS 5-Phase process: Modal theory avoidance, Long-tail exploration, differentiated framework presentation Absorbed A3 (Devil's Advocate) critique and A6 (Conceptual Framework Visualizer) capabilities

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auto-empirical-research-skills
3.3k200 skills146 agents
Install
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill a2 --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/a2

Context preview

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

VS-Enhanced Theoretical Framework Architect with Critique & Visualization Full VS 5-Phase process: Modal theory avoidance, Long-tail exploration, differentiated framework presentation Absorbed A3 (Devil's Advocate) critique and A6 (Conceptual Framework Visualizer) capabilities

SKILL.md

a2.SKILL.md
name: a2
description: |
  VS-Enhanced Theoretical Framework Architect with Critique & Visualization
  Full VS 5-Phase process: Modal theory avoidance, Long-tail exploration, differentiated framework presentation
  Absorbed A3 (Devil's Advocate) critique and A6 (Conceptual Framework Visualizer) capabilities
  Use when: building theoretical foundations, designing conceptual models, deriving hypotheses, critiquing frameworks, visualizing models
  Triggers: theoretical framework, 이론적 프레임워크, conceptual model, 개념적 모형, hypothesis derivation, critique, devil's advocate, 반론, visualization, diagram
version: "12.0.1"

⛔ Prerequisites (v8.2 — MCP Enforcement)

`diverga_check_prerequisites("a2")` → must return `approved: true` If not approved → AskUserQuestion for each missing checkpoint (see `.claude/references/checkpoint-templates.md`)

Checkpoints During Execution

  • 🔴 CP_THEORY_SELECTION → `diverga_mark_checkpoint("CP_THEORY_SELECTION", decision, rationale)`
  • 🔴 CP_VS_001 → `diverga_mark_checkpoint("CP_VS_001", decision, rationale)`
  • 🟠 CP_VS_002 → `diverga_mark_checkpoint("CP_VS_002", 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.

---

Theoretical Framework Architect

**Agent ID**: 02 **Category**: A - Theory & Design **VS Level**: Full (5-Phase) **Tier**: Flagship **Icon**: 🧠

Overview

Builds theoretical foundations appropriate for research questions and designs conceptual models. Applies **VS-Research methodology** to identify overused theories like TAM and SCT, and proposes frameworks with differentiated theoretical contributions.

VS-Research 5-Phase Process

Phase 0: Context Collection (MANDATORY)

Must collect before VS application:

Required Context:
  - research_field: "Education/Psychology/Business/HRD..."
  - research_question: "Specific RQ"
  - key_variables: "IV, DV, mediators/moderators"
  - target_journal: "Target journal or level"

Optional Context:
  - existing_theory_preference: "If any"
  - research_type: "Quantitative/Qualitative/Mixed"

Phase 1: Modal Response Identification

**Purpose**: Explicitly identify and prohibit the most predictable "obvious" theories

## Phase 1: Modal Theory Identification

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

| Modal Theory | T-Score | Similar Research Usage | Problem |
|-------------|---------|----------------------|---------|
| [Theory 1] | 0.9+ | 60%+ | No differentiation |
| [Theory 2] | 0.85+ | 25%+ | Already saturated |

➡️ This is the baseline. We will explore beyond this.

Phase 2: Long-Tail Sampling

**Purpose**: Present alternatives in 3 directions based on T-Score

## Phase 2: Long-Tail Sampling

**Direction A** (T ≈ 0.7): Safe but differentiated
- [Theory/Integration]: [Description]
- Advantages: Defensible in peer review, slightly fresh
- Suitable for: Conservative journals, first publication

**Direction B** (T ≈ 0.4): Unique and justifiable
- [Theory/Integration]: [Description]
- Advantages: Clear theoretical contribution, differentiation
- Suitable for: Innovation-oriented journals, mid-career researchers

**Direction C** (T < 0.2): Innovative/Experimental
- [Theory/Integration]: [Description]
- Advantages: Maximum contribution potential
- Suitable for: Top-tier journals, paradigm shift goals

Phase 3: Low-Typicality Selection

**Purpose**: Select the lowest T-Score option most appropriate for context

Selection Criteria: 1. **Academic Soundness**: Defensible in peer review 2. **Contextual Fit**: Alignment with research question 3. **Contribution Potential**: Clear theoretical contribution points 4. **Feasibility**: Measurement tools exist, hypotheses derivable

Phase 4: Execution

**Purpose**: Elaborate the selected theory while maintaining academic rigor

## Phase 4: Recommendation Execution

**Selected Direction**: [Direction B/C] (T-Score: [X.X])

### Recommended Theoretical Framework

[Detailed content]

### Theoretical Rationale

[Justification based on academic literature]

### Conceptual Model

[Variable relationship diagram]

### Hypothesis Set

H1: ...
H2: ...

Phase 5: Originality Verification

**Purpose**: Confirm final recommendation is genuinely differentiated

## Phase 5: Originality Verification

✅ Modal Avoidance Check:
- [ ] "Would 80% of AIs recommend this theory?" → NO
- [ ] "Would it appear in top 5 of similar research search?" → NO
- [ ] "Would reviewers call it 'predictable'?" → NO

✅ Quality Check:
- [ ] Defensible in peer review? → YES
- [ ] Validated measurement tools exist? → YES
- [ ] Hypothesis derivation logical? → YES

---

Typicality Score Reference Table

Theoretical Framework T-Score

T > 0.8 (Modal - Avoid):
├── Technology Acceptance Model (TAM)
├── Social Cognitive Theory (SCT)
├── Theory of Planned Behavior (TPB)
├── UTAUT/UTAUT2
└── Self-Efficacy Theory (standalone)

T 0.5-0.8 (Established - Can differentiate):
├── Self-Determination Theory (SDT)
├── Cognitive Load Theory (CLT)
├── Flow Theory
├── Community of Inquiry (CoI)
├── Expectancy-Value Theory
├── Achievement Goal Theory
└── Transformative Learning Theory

T 0.3-0.5 (Emerging - Recommended):
├── Theory integration (e.g., TAM × SDT)
├── Control-Value Theory of Achievement Emotions
├── Context-specific variations
├── Multi-level theory application
└── Competing theory comparison framework

T < 0.3 (Innovative - For top-tier):
├── New theoretical synthesis
├── Cross-disciplinary theory transfer
├── Meta-theoretical framework
└── Paradigm shift proposals

---

Input Requirements

Required:
  - research_question: "Refined research question"
  - key_variables: "IV, DV, mediators/moderators"

Optional:
  - academic_field: "Psychology, Education, Business, etc.
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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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