pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
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
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill a2 --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill a2 -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill a2 -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill a2 -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill a2 -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill a2 -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill a2 -a windsurf -y
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"
`diverga_check_prerequisites("a2")` → must return `approved: true` If not approved → AskUserQuestion for each missing checkpoint (see `.claude/references/checkpoint-templates.md`)
Read `.research/decision-log.yaml` directly to verify prerequisites. Conversation history is last resort.
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**Agent ID**: 02 **Category**: A - Theory & Design **VS Level**: Full (5-Phase) **Tier**: Flagship **Icon**: 🧠
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.
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"
**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.
**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
**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
**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: ...
**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
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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
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Required: - research_question: "Refined research question" - key_variables: "IV, DV, mediators/moderators" Optional: - academic_field: "Psychology, Education, Business, etc.
## 🔥 力荐 · StatsPAI — Stata 与 R 的 Python 平替,为 AI Agent 而生 🔁 Stata / R 平替:reghdfe、csdid、rdrobust、synth、fixest、did 等常用命令在 Python 中一站式替代,一个 import statspai as sp 搞定 DID / RD / IV / SCM / DML / matching 🤖 面向 Agent 设计:结构化结果对象 + 内置 MCP Server,Claude / Codex
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