pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Diverga v12.0 setup wizard. 4-step researcher profile setup. Captures discipline, experience, tools, database access, and Agent Teams + VS Arena preference. Triggers: setup, configure, 설정, install
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill setup --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill setup -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill setup -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill setup -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill setup -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill setup -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill setup -a windsurf -y
name: setup description: | Diverga v12.0 setup wizard. 4-step researcher profile setup. Captures discipline, experience, tools, database access, and Agent Teams + VS Arena preference. Triggers: setup, configure, 설정, install version: "12.0.1"
**Version**: 12.0.0 **Trigger**: `/diverga:setup`
Diverga setup wizard. 4 steps: Researcher Profile, Tools & Access, Agent Teams + VS Arena, Config Generation. Captures information that genuinely changes agent behavior.
1. **Only ask what agents actually use** — no dead config fields 2. **Lazy config for rare features** — OpenAlex email, citation format, qual software are asked when the relevant agent runs for the first time 3. **Researcher profile is stable** — discipline and tools don't change per project; checkpoints handle project-specific decisions
When user invokes `/diverga:setup`, execute this interactive wizard:
Check for existing config:
Display welcome message, then ask TWO questions using a single AskUserQuestion call:
╔══════════════════════════════════════════════════════════════════╗ ║ Welcome to Diverga v12.0 ║ ║ AI Research Assistant - 29 Agents (24 core + 5 VS Arena) ║ ╠══════════════════════════════════════════════════════════════════╣ ║ ║ ║ Diverga adapts to your background. ║ ║ A doctoral student gets more scaffolding. ║ ║ An experienced faculty gets concise recommendations. ║ ║ Your preferred tools determine code and database suggestions. ║ ║ ║ ╚══════════════════════════════════════════════════════════════════╝
questions:
- question: "What is your primary research discipline?"
header: "Discipline"
multiSelect: false
options:
- label: "Education"
description: "Educational Technology, Curriculum, Higher Ed, STEM Ed, etc."
- label: "Psychology"
description: "Clinical, Developmental, Social, Cognitive, I/O, etc."
- label: "Health Sciences"
description: "Public Health, Nursing, Medicine, Rehabilitation, etc."
- label: "Social Sciences"
description: "Sociology, Political Science, Communication, Business, etc."
# User can also type a custom discipline via "Other"
- question: "What is your research experience level?"
header: "Experience"
multiSelect: false
options:
- label: "Doctoral Student"
description: "Currently pursuing PhD/EdD. More guidance on methodology and analysis."
- label: "Postdoc / Early Career"
description: "Completed doctorate. Familiar with research process."
- label: "Faculty / Senior Researcher"
description: "Experienced researcher. Concise recommendations preferred."**How this affects agents:**
Ask TWO questions using a single AskUserQuestion call:
questions:
- question: "Which statistical software do you use?"
header: "Stats Tools"
multiSelect: true
options:
- label: "R"
description: "tidyverse, lavaan, lme4, etc."
- label: "Python"
description: "pandas, statsmodels, scikit-learn, etc."
- label: "SPSS"
description: "IBM SPSS Statistics"
- label: "Stata"
description: "Stata/SE or Stata/MP"
- question: "Which academic databases can you access?"
header: "DB Access"
multiSelect: true
options:
- label: "Scopus"
description: "Elsevier Scopus (institutional subscription)"
- label: "Web of Science"
description: "Clarivate WoS (institutional subscription)"
- label: "PsycINFO"
description: "APA PsycINFO (institutional subscription)"
- label: "ERIC + Semantic Scholar (free)"
description: "Always available, no subscription needed"**How this affects agents:**
question: "Configure multi-agent collaboration mode"
header: "Agent Teams"
options:
- label: "Agent Teams + VS Arena (Recommended)"
description: "Full parallel execution with inter-agent communication. VS Arena debates use real cross-critique between personas. Requires Claude Code v2.1.32+. Higher token usage."
- label: "Subagents + Classic VS (Default)"
description: "Agents run as subagents. VS Arena generates options from single agent (no cross-critique). Lower cost."
- label: "Disabled"
description: "No multi-agent features. Single agent execution only."**If "Agent Teams + VS Arena" selected**, add to config:
{
"agent_teams": { "enabled": true },
"vs_arena": { "enabled": true, "team_size": 3, "cross_critique": true }
}Also set `CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1` in user's `settings.json` env: 1. Write to `~/.claude/settings.json`:
{ "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" } }(merge with existing settings, don't overwrite)
**If "Subagents + Classic VS" selected**, add to config:
{
"agent_teams": { "enabled": false },
"vs_arena": { "enabled": true, "team_size": 3, "cross_## 🔥 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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