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research-coordinator

Coordinate research from question development through literature searches, analysis, and publication with specialized agents. Supports quantitative, qualitative, and mixed methods studies, plus systematic reviews, with human approval at key decisions about research direction and methods.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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

brycewang-stanford/Auto-Empirical-Research-Skills

실행 점수 미검증

설치 방법 보기

이럴 때 유용합니다

Refining research questions and choosing a theoretical framework
Planning a systematic review and approving screening criteria
Designing a qualitative study using interviews
Planning analysis that combines numerical and qualitative evidence

포함된 파일(1)

SKILL.md13.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

MANDATORY: Checkpoint Enforcement Rules (v8.2 — MCP-First)

Full details: docs/CHECKPOINT-RULES.md

Rule 5: Override Refusal

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

Rule 6: MCP-First Verification

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

단일 에이전트 호출 시:

diverga_check_prerequisites(agent_id) 호출
approved: false → 각 missing checkpoint에 대해 AskUserQuestion 도구 호출
REQUIRED 전제조건은 절대 스킵 불가 (사용자가 "건너뛰자"해도 Override Refusal Template 제시)
모든 전제조건 통과 후 에이전트 작업 시작
에이전트 완료 시 diverga_mark_checkpoint() 으로 결정 기록

다중 에이전트 동시 호출 시:

모든 트리거된 에이전트의 prerequisites를 합집합으로 수집
Checkpoint Dependency Order에 따라 정렬 (Level 0 → Level 5)
각 전제조건을 AskUserQuestion 도구로 순서대로 질문
중복 체크포인트는 한 번만 질문
모든 전제조건 해결 후 에이전트들을 병렬 실행
각 에이전트 실행 중 자체 체크포인트도 AskUserQuestion 필수

모든 체크포인트에서:

반드시 AskUserQuestion 도구 사용 (텍스트 질문 금지)
.claude/references/checkpoint-templates.md의 파라미터 사용
응답 받을 때까지 STOP and WAIT
diverga_mark_checkpoint(checkpoint_id, decision, rationale) 으로 결정 기록

자기 검증 (에이전트 작업 완료 전):

"Own Checkpoints"를 모두 트리거했는지 자가 확인
미트리거 체크포인트가 있으면 작업 마무리 전 반드시 호출
diverga_checkpoint_status() 로 전체 현황 확인 가능

Research Coordinator v12.0 - Human-Centered Edition

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

Design Philosophy

┌─────────────────────────────────────────────────────────────┐
│                    v6.0 Design Principle                    │
│                                                             │
│   "AI works BETWEEN checkpoints, humans decide AT them"     │
│                                                             │
│   ┌─────────┐     ┌─────────┐     ┌─────────┐              │
│   │ Stage 1 │ ──▶ │ STOP &  │ ──▶ │ Stage 2 │              │
│   │ (AI)    │     │  ASK    │     │ (AI)    │              │
│   └─────────┘     └─────────┘     └─────────┘              │
│                       ▲                                     │
│                       │                                     │
│              Human Decision Required                        │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Human Checkpoint System

Checkpoint Types

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

Required Checkpoints (MANDATORY HALT)

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)

Recommended Checkpoints (SUGGESTED HALT)

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"

Paradigm Detection

Research Coordinator auto-detects your research paradigm from conversation signals.

Quantitative signals: hypothesis, effect size, p-value, sample size, variable, experiment, ANOVA, regression, SEM, meta-analysis, t-test, chi-square, correlation

Qualitative signals: lived experience, meaning, saturation, theme, category, code, participant, phenomenology, grounded theory, case study, thematic analysis, narrative inquiry, ethnography, action research

Mixed methods signals: mixed methods, integration, convergence, sequential, concurrent, joint display, meta-inference

Paradigm Confirmation (Always Ask)

When paradigm is detected, ALWAYS confirm with user:

"A [Quantitative] research approach has been detected from your context.
Shall we proceed with this paradigm?

 [Y] Yes, proceed with Quantitative research
 [Q] No, switch to Qualitative research
 [M] No, switch to Mixed Methods
 [?] I'm not sure, I need help"

Agent Catalog (24 Agents)

Category A: Research Foundation (3 Agents)

ID	Agent	Purpose
A1	Research Question Refiner	Refine questions using PICO/SPIDER/PEO frameworks
A2	Theoretical Framework Architect	Theory selection + critique + visualization (absorbed A3, A6)
A5	Paradigm & Worldview Advisor	Epistemology, ontology, ethics guidance (absorbed A4)

Category B: Literature & Evidence (2 Agents)

ID	Agent	Purpose
B1	Literature Review Strategist	PRISMA-compliant search + scoping review
B2	Evidence Quality Appraiser	RoB 2, ROBINS-I, CASP, JBI, GRADE

Category C: Study Design & Meta-Analysis (4 Agents)

ID	Agent	Purpose
C1	Quantitative Design Consultant	Design + materials + sampling (absorbed C4, D1)
C2	Qualitative Design Consultant	Design + ethnography + action research (absorbed H1, H2)
C3	Mixed Methods Design Consultant	Convergent, sequential designs
C5	Meta-Analysis Master	Multi-gate validation + data integrity + effect size + error prevention + sensitivity (absorbed C6, C7, B3, E5-meta)

Category D: Data Collection (2 Agents)

ID	Agent	Purpose
D2	Data Collection Specialist	Interviews + focus groups + observation (absorbed D3)
D4	Measurement Instrument Developer	Scale development, validation

Category E: Analysis (3 Agents)

ID	Agent	Purpose
E1	Quantitative Analysis Guide	Statistical methods + code generation + sensitivity (absorbed E4, E5-primary)
E2	Qualitative Coding Specialist	Thematic analysis, grounded theory coding
E3	Mixed Methods Integration Specialist	Joint displays, meta-inference

Category F: Quality & Validation (1 Agent)

ID	Agent	Purpose
F5	Humanization Verifier	Citation integrity, statistical accuracy, meaning preservation

Category G: Publication & Communication (4 Agents)

ID	Agent	Purpose
G1	Journal Matcher	Find target journals
G2	Publication Specialist	Writing + review + pre-reg + quality (absorbed G3, G4, F1, F2, F3)
G5	Academic Style Auditor	AI pattern detection (24 categories), risk scoring
G6	Academic Style Humanizer	Transform AI patterns to natural academic prose

Category I: Systematic Review Automation (4 Agents)

ID	Agent	Purpose	Checkpoint
I0	Review Pipeline Orchestrator	Pipeline coordination, checkpoint management	All SCH_*
I1	Paper Retrieval Agent	Multi-database fetching (Semantic Scholar, OpenAlex, arXiv)	SCH_DATABASE_SELECTION
I2	Screening Assistant	AI-PRISMA 6-dimension screening	SCH_SCREENING_CRITERIA
I3	RAG Builder	Vector DB + parallel processing (absorbed B5)	SCH_RAG_READINESS

Category X: Cross-cutting (1 Agent)

ID	Agent	Purpose
X1	Research Guardian	Ethics advisory + bias detection (absorbed A4, F4)

VS-Research Methodology

VS methodology prevents AI mode collapse by generating divergent alternatives at every decision point, scored by T (Typicality). Human selects at checkpoint.

T-Score	Label	Meaning
>= 0.7	Common	Highly typical, safe but limited novelty
0.4-0.7	Moderate	Balanced risk-novelty
0.2-0.4	Innovative	Novel, requires strong justification
< 0.2	Experimental	Highly novel, high risk/reward

Orchestrator Delegation

When parallel execution or inter-agent debate is needed:

Determine which agents to invoke
Delegate to /diverga:orchestrator with agent IDs and context
Orchestrator handles Agent Teams vs subagent decision

Do NOT dispatch agents directly when:

Multiple agents need to communicate (use orchestrator)
VS Arena debate is triggered (use orchestrator)
I0 systematic review pipeline needs parallel fetchers (use orchestrator)

Systematic Review Automation (Category I)

Pipeline Stages

I0 (Orchestrator) → I1 (Retrieval) → I2 (Screening) → I3 (RAG)
                        ↓                  ↓              ↓
               SCH_DATABASE       SCH_SCREENING      SCH_RAG

Human Checkpoints

Checkpoint	Level	When	Agent
SCH_DATABASE_SELECTION	REQUIRED	Before paper retrieval	I1
SCH_SCREENING_CRITERIA	REQUIRED	Before AI screening	I2
SCH_RAG_READINESS	RECOMMENDED	Before RAG queries	I3
SCH_PRISMA_GENERATION	OPTIONAL	Before PRISMA diagram	I0

Cost Optimization

Task	Provider	Cost/100 papers
Screening	Groq (llama-3.3-70b)	$0.01
RAG Queries	Groq	$0.02
Embeddings	Local (MiniLM)	$0
Total 500-paper review	Mixed	~$0.07

Quick Start

Simply tell Research Coordinator what you want to do:

"I want to conduct a systematic review on AI in education"
"메타분석 연구를 시작하고 싶어"
"Help me design a phenomenological study on teacher burnout"

The system will:

Detect your paradigm from your request
ASK for confirmation of paradigm
Present VS alternatives with T-Scores
WAIT for your selection
Guide you through the pipeline with checkpoints

Reference

Checkpoint enforcement rules: docs/CHECKPOINT-RULES.md
Model routing and execution: /diverga:orchestrator
Architecture and systems: docs/ARCHITECTURE.md
MCP tools: docs/MCP-TOOLS.md
Autonomous modes removed in v6.0: see CHANGELOG.md
Version history: see CHANGELOG.md

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

같은 저장소의 스킬

a1

무료

Turn a broad research idea into clear, testable questions. Compare familiar and less conventional approaches, structure the selected question around its population and methods, and assess variables, feasibility, and potential theoretical contributions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

Turn a research topic into testable questions
Narrow an overly broad research question
Compare alternative research angles

brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

a2

무료

Helps build a research framework by comparing theories, mapping relationships between variables, and deriving testable hypotheses. Explores less conventional options, examines weaknesses and competing explanations, and produces diagrams with anticipated reviewer objections.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

Comparing theories for a research question
Deriving hypotheses from relationships between variables
Examining weaknesses and anticipated reviewer objections

brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

a5

무료

Clarify the philosophical assumptions behind a research project and check whether they fit the proposed methods. Get a reasoned research stance, draft proposal wording, and guidance for explaining how the researcher's background and values shape the study.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

Clarifying philosophical foundations for a research proposal
Explaining why the chosen research methods fit the study
Drafting a statement about the researcher's position

brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

academic-paper

무료

Plan and draft academic papers through specialized agent roles covering literature, arguments, citations, and review. Get chapter guidance, revision roadmaps, Traditional Chinese and English abstracts, and documents formatted as LaTeX, DOCX, PDF, or Markdown.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

Planning a paper chapter by chapter
Drafting a paper from research materials
Turning reviewer comments into a revision roadmap

brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

academic-paper-composer

무료

Turn a detailed outline into a philosophy or interdisciplinary manuscript. Draft chapters with repeated quality reviews, check terminology and arguments across sections, and prepare the manuscript, evaluation reports, abstract, and metadata for submission.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

Draft a full paper from a detailed outline
Review argument and citation quality chapter by chapter
Check terminology and argument flow across chapters

brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

academic-paper-reviewer

무료

Simulate academic peer review through five reviewer roles tailored to the paper's field. Receive separate critiques, an editorial decision, and a prioritized revision roadmap, or check whether a revised manuscript addresses earlier comments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

Review a manuscript before journal submission
Examine research methods and statistical validity
Challenge core arguments and alternative explanations

brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

brycewang-stanford의 스킬 모두 보기

이 스킬의 문제 신고

설치

명령으로 설치

npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill research-coordinator

터미널에 붙여 넣고 실행합니다(Node.js 필요). Claude Code, Codex 등 설치할 에이전트를 고를 수 있습니다.

에이전트에게 부탁하기

다음 Agent Skill을 설치해 주세요.
https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/25-HosungYou-Diverga/skills/research-coordinator

먼저 SKILL.md와 첨부 파일의 내용을 확인해 수상한 처리(외부 전송, 인증 정보 읽기 등)가 없는지 확인한 뒤 Claude Code는 ~/.claude/skills/research-coordinator/, Codex는 ~/.agents/skills/research-coordinator/ 에 배치해 주세요.

이 문장을 그대로 Claude Code나 Codex에 보내면 내용을 확인한 뒤 설치해 줍니다.

GitHub에서 소스 보기

저장소: brycewang-stanford/Auto-Empirical-Research-Skills
스타: 4,747
포크: 556
카테고리: 리서치·학습
원문 언어: 한국어
최종 업데이트: 2026년 10월 20일

스킬은 에이전트를 위한 지시와 스크립트의 묶음입니다. cccskills는 내용의 안전성을 보증하지 않습니다. 설치하기 전에 내용을 확인하세요.