pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Diverga help guide - displays all 24 agents across 9 categories, commands, and usage examples. Triggers: help, guide, how to use, 도움말
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill help --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill help -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill help -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill help -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill help -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill help -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill help -a windsurf -y
name: help description: | Diverga help guide - displays all 24 agents across 9 categories, commands, and usage examples. Triggers: help, guide, how to use, 도움말 version: "12.0.1"
**Version**: 2.0.0 **Trigger**: `/diverga:help`
Displays comprehensive guide for Diverga, including all 24 agents across 9 categories, commands, and usage examples.
When user invokes `/diverga:help`, display:
╔══════════════════════════════════════════════════════════════════╗ ║ Diverga v11.0 Help ║ ║ AI Research Assistant - 24 Agents, 9 Categories ║ ╚══════════════════════════════════════════════════════════════════╝ ┌─────────────────────────────────────────────────────────────────┐ │ QUICK START │ ├─────────────────────────────────────────────────────────────────┤ │ Just describe your research: │ │ "I want to conduct a meta-analysis on AI in education" │ │ "Help me design a qualitative study" │ │ "메타분석 연구를 시작하고 싶어" │ │ │ │ Diverga auto-detects context and activates relevant agents. │ └─────────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ COMMANDS │ ├─────────────────────────────────────────────────────────────────┤ │ /diverga:setup Initial configuration wizard │ │ /diverga:doctor System diagnostics & health check │ │ /diverga:help This help guide │ │ /diverga:meta-analysis Meta-analysis workflow (C5) │ │ /diverga:humanize Humanization pipeline (G5+G6+F5) │ └─────────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ CATEGORY A: FOUNDATION (3 agents) │ ├─────────────────────────────────────────────────────────────────┤ │ diverga:a1 ResearchQuestionRefiner Refine research Qs │ │ diverga:a2 TheoreticalFrameworkArchitect Frameworks + Critique │ │ + Visualization (absorbed A3, A6) │ │ diverga:a5 ParadigmWorldviewAdvisor Ontology + Ethics │ └─────────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ CATEGORY B: EVIDENCE (2 agents) │ ├─────────────────────────────────────────────────────────────────┤ │ diverga:b1 LiteratureReviewStrategist Literature search │ │ diverga:b2 EvidenceQualityAppraiser RoB, GRADE appraisal │ └─────────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ CATEGORY C: DESIGN & META-ANALYSIS (4 agents) │ ├─────────────────────────────────────────────────────────────────┤ │ diverga:c1 QuantitativeDesignConsultant Quant design │ │ + Materials + Sampling (absorbed C4, D1) │ │ diverga:c2 QualitativeDesignConsultant Qual design │ │ + Ethnography + Action Research (absorbed H1, H2) │ │ diverga:c3 MixedMethodsDesignConsultant Mixed methods │ │ diverga:c5 MetaAnalysisMaster ⭐ Meta-analysis lead │ │ + Data/Effect/Error/Sensitivity (absorbed C6,C7,B3,E5)│ └─────────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ CATEGORY D: DATA COLLECTION (2 agents) │ ├─────────────────────────────────────────────────────────────────┤ │ diverga:d2 DataCollectionSpecialist Interview + Observation │ │ (absorbed D3, renamed) │ │ diverga:d4 MeasurementInstrumentDeveloper Instrument dev │ └─────────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ CATEGORY E: ANALYSIS (3 agents) │ ├─────────────────────────────────────────────────────────────────┤ │ diverga:e1 QuantitativeAnalysisGuide Statistical guidance │ │ + Code Gen + Sensitivity (absorbed E4, E5) │ │ diverga:e2 QualitativeCodingSpecialist Qualitative coding │ │ diverga:e3 MixedMethodsIntegration Integration methods │ └─────────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ CATEGORY F: QUALITY (1 agent) │ ├─────────────────────────────────────────────────────────────────┤ │ diverga:f5 HumanizationVerifier Verify humanization │ └─────────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ CATEGORY G: COMMUNICATION (4 agents) │ ├─────────────────────────────────────────────────────────────────┤ │ diverga:g1 JournalMatcher Match journals │ │ diverga:g2 PublicationSpecialist Writing + Review + PreReg│ │ + Quality (absorbed G3, G4, F1, F2, F3) │ │ diverga:g5 AcademicStyleAuditor AI pattern detection │ │ diverga:g6 AcademicStyleHumanizer Humanize AI text │ └─────────────────────────────────────────────────────────────────┘ ┌─────────────────────────────────────────────────────────────────┐ │ CATEGORY I: SYSTEMATIC REVIEW (4 agents) │ ├─────────────────────────────────────────────────────────────────┤ │ diverga:i0 ReviewPipelineOrchestrator Pipeline coordination │ │ diverga:i1 Pa
## 🔥 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
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics…
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid +…
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven +…
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready…
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers…
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling…