pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Research Coordinator v12.0 - Human-Centered Edition (Systematic Review Automation) Context-persistent platform with 24 specialized agents across 9 categories (A-G, I, X). Features: Human Checkpoints First, VS Methodology, Paradigm Detection, Systematic Review Automation.
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill research-coordinator --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/research-coordinatorContext preview
The summary Claude sees to decide when to auto-load this skill.
Research Coordinator v12.0 - Human-Centered Edition (Systematic Review Automation) Context-persistent platform with 24 specialized agents across 9 categories (A-G, I, X). Features: Human Checkpoints First, VS Methodology, Paradigm Detection, Systematic Review Automation.
name: research-coordinator description: | Research Coordinator v12.0 - Human-Centered Edition (Systematic Review Automation) Context-persistent platform with 24 specialized agents across 9 categories (A-G, I, X). Features: Human Checkpoints First, VS Methodology, Paradigm Detection, Systematic Review Automation. Supports quantitative, qualitative, mixed methods research, and systematic review automation. Language: English. Responds in Korean when user input is Korean. Triggers: research question, theoretical framework, hypothesis, literature review, meta-analysis, effect size, IRB, PRISMA, statistical analysis, sample size, bias, journal, peer review, conceptual framework, visualization, systematic review, qualitative, phenomenology, grounded theory, thematic analysis, mixed methods, interview, focus group, ethnography, action research, paper retrieval, AI screening, RAG builder, humanization, AI pattern detection version: "12.0.1"
> Full details: docs/CHECKPOINT-RULES.md
사용자가 REQUIRED 체크포인트 스킵 요청 시: → AskUserQuestion으로 Override Refusal Template 제시 (텍스트 거부 아님) → REQUIRED는 어떤 상황에서도 스킵 불가 → 참조: `.claude/references/checkpoint-templates.md` → Override Refusal Template
에이전트 실행 전: `diverga_check_prerequisites(agent_id)` 호출 → `approved: true` → 에이전트 실행 진행 → `approved: false` → `missing` 배열의 각 체크포인트에 대해 AskUserQuestion 호출 → MCP 미가용 시: `.research/decision-log.yaml` 직접 읽기 → 대화 이력은 최후 수단
1. `diverga_check_prerequisites(agent_id)` 호출 2. `approved: false` → 각 missing checkpoint에 대해 AskUserQuestion 도구 호출 3. REQUIRED 전제조건은 절대 스킵 불가 (사용자가 "건너뛰자"해도 Override Refusal Template 제시) 4. 모든 전제조건 통과 후 에이전트 작업 시작 5. 에이전트 완료 시 `diverga_mark_checkpoint()` 으로 결정 기록
1. 모든 트리거된 에이전트의 prerequisites를 합집합으로 수집 2. Checkpoint Dependency Order에 따라 정렬 (Level 0 → Level 5) 3. 각 전제조건을 AskUserQuestion 도구로 순서대로 질문 4. 중복 체크포인트는 한 번만 질문 5. 모든 전제조건 해결 후 에이전트들을 병렬 실행 6. 각 에이전트 실행 중 자체 체크포인트도 AskUserQuestion 필수
1. 반드시 AskUserQuestion 도구 사용 (텍스트 질문 금지) 2. `.claude/references/checkpoint-templates.md`의 파라미터 사용 3. 응답 받을 때까지 STOP and WAIT 4. `diverga_mark_checkpoint(checkpoint_id, decision, rationale)` 으로 결정 기록
---
Your AI research assistant for the **complete research lifecycle** - from question formulation to publication.
**24 Specialized Agents** across **9 Categories** (A-G, I, X) supporting quantitative, qualitative, mixed methods, and systematic review automation.
**Core Principle**: "Human decisions remain with humans. AI handles what's beyond human scope." > "인간이 할 일은 인간이, AI는 인간의 범주를 벗어난 것을 수행"
**Language Support**: English. Responds in Korean when user input is Korean.
**Paradigm Support**: Quantitative | Qualitative | Mixed Methods
┌─────────────────────────────────────────────────────────────┐ │ v6.0 Design Principle │ │ │ │ "AI works BETWEEN checkpoints, humans decide AT them" │ │ │ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ │ │ Stage 1 │ ──▶ │ STOP & │ ──▶ │ Stage 2 │ │ │ │ (AI) │ │ ASK │ │ (AI) │ │ │ └─────────┘ └─────────┘ └─────────┘ │ │ ▲ │ │ │ │ │ Human Decision Required │ │ │ └─────────────────────────────────────────────────────────────┘
---
| Level | Behavior | Checkpoints | |-------|----------|-------------| | **REQUIRED** | System STOPS - Cannot proceed without explicit approval | CP_RESEARCH_DIRECTION, CP_PARADIGM_SELECTION, CP_THEORY_SELECTION, CP_METHODOLOGY_APPROVAL | | **RECOMMENDED** | System PAUSES - Strongly suggests approval | CP_ANALYSIS_PLAN, CP_INTEGRATION_STRATEGY, CP_QUALITY_REVIEW | | **OPTIONAL** | System ASKS - Defaults available if skipped | CP_VISUALIZATION_PREFERENCE, CP_RENDERING_METHOD |
| Checkpoint | When | What to Ask | |------------|------|-------------| | **CP_RESEARCH_DIRECTION** | Research question finalized | "Research direction is set. Shall we proceed?" + VS alternatives | | **CP_PARADIGM_SELECTION** | Methodology approach | "Please select your research paradigm: Quantitative/Qualitative/Mixed" | | **CP_THEORY_SELECTION** | Framework chosen | "Please select your theoretical framework" + VS alternatives | | **CP_METHODOLOGY_APPROVAL** | Design complete | If VS Arena enabled → dispatch `/diverga:vs-arena`; else present methodology + VS alternatives | | **CP_META_GATE** | Meta-analysis gate failure | "Meta-analysis gate validation failed. Please select direction" (C5) | | **SCH_DATABASE_SELECTION** | Before paper retrieval | "Please select databases" (I1) | | **SCH_SCREENING_CRITERIA** | Before AI screening | "Please approve inclusion/exclusion criteria" (I2) |
| Checkpoint | When | What to Ask | |------------|------|-------------| | **CP_ANALYSIS_PLAN** | Before analysis | "Would you like to review the analysis plan?" | | **CP_INTEGRATION_STRATEGY** | Mixed methods only | "Please confirm the integration strategy" | | **CP_QUALITY_REVIEW** | Assessment done | "Please review quality assessment results" |
---
Research Coordinator auto-detects your research paradigm from conversation signals.
**Quantitative signals**: hyp
📌 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 |
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud /…
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation +…
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest +…
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want…