VS-Enhanced Qualitative Design Consultant with Ethnography & Action Research Enhanced VS 3-Phase process: Avoids overused phenomenology, proposes context-optimal qualitative strategies Absorbed H1 (Ethnographic Research Advisor) and H2 (Action Research Facilitator) capabilities Use when: selecting qualitative research design, planning phenomenology/grounded theory/case study/ethnography/action research Triggers: phenomenology, 현상학, grounded theory, 근거이론, case study, 사례연구, narrative inquiry, ethnography, 민족지, action research, 실행연구, qualitative design
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brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
VS-Enhanced Quantitative Design Consultant with Materials & Sampling Enhanced VS 3-Phase process: Avoids obvious experimental designs, proposes context-optimal quantitative strategies Absorbed C4 (Experimental Materials Developer) and D1 (Sampling Strategy Advisor) capabilities Use when: selecting quantitative research design, planning experimental/survey methodology, power analysis, developing materials, sampling Triggers: RCT, quasi-experimental, experimental design, survey design, power analysis, sample size, factorial design, materials, stimuli, sampling strategy
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
VS-Enhanced Journal Matcher with Journal Intelligence MCP — Real-time journal data pipeline with checkpoint-based human decisions. Uses OpenAlex + Crossref APIs for live metrics. Light VS applied: Avoids IF-centric recommendations + multi-dimensional matching strategy Use when: selecting target journals, planning submissions, comparing publication options Triggers: journal, submission, impact factor, academic journal, publication, submit
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
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
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Diverga help guide - displays all 24 agents across 9 categories, commands, and usage examples. Triggers: help, guide, how to use, 도움말
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
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
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
VS-Enhanced Literature Review Strategist - Comprehensive support for multiple review methodologies Full VS 5-Phase process: Prevents Mode Collapse and presents creative search strategies Supports: Systematic Review (PRISMA 2020), Scoping Review (JBI/PRISMA-ScR), Meta-Synthesis, Realist Synthesis, Narrative Review, Rapid Review Use when: conducting any type of literature review, systematic reviews, meta-analyses, scoping reviews, finding prior research Triggers: literature review, PRISMA, systematic review, scoping review, meta-synthesis, realist synthesis, narrative review, rapid review
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Use for everyday coding tasks that involve writing or modifying source code.
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Agent A5 - Paradigm & Worldview Advisor - Philosophical foundations for research design. Covers ontology, epistemology, axiology, and methodology alignment. Use when: establishing philosophical foundations, justifying methodological choices, writing positionality statements Triggers: paradigm, 패러다임, ontology, epistemology, worldview, 세계관, philosophical foundations, 철학적 기초
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
E1-Quantitative Analysis Guide with Code Generation & Sensitivity Analysis VS-Enhanced with Full 5-Phase process: Avoids obvious analyses, explores innovative methodologies Expanded to include qualitative analysis (thematic, grounded theory, content, narrative) Absorbed E4 (Analysis Code Generator) and E5 (Sensitivity Analysis - Primary Study) capabilities Use when: selecting statistical/qualitative methods, interpreting results, checking assumptions, generating code, sensitivity analysis Triggers: statistical analysis, ANOVA, regression, t-test, power analysis, assumption checking, effect size, thematic analysis, grounded theory, content analysis, narrative analysis, NVivo, ATLAS.ti, coding, qualitative data, R code, Python code, SPSS syntax, sensitivity analysis, robustness check
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Use only when creating new registrable ML components that require Factory or Registry patterns.
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Diverga Memory System v7.0 - Context-persistent research support with checkpoint auto-trigger and cross-session continuity. Triggers: memory, remember, context, recall, checkpoint, decision, persist, 기억, 맥락, 세션, 체크포인트
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Humanization Quality Verifier - Ensures transformation integrity and quality Validates that humanization preserves meaning, citations, and academic standards Use when: after G6 transformation, before final export, for quality assurance Triggers: verify humanization, check transformation, validate changes
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Agent E2 - Qualitative Coding Specialist - Systematic coding and theme development. Covers codebook development, coding strategies, saturation assessment, and CAQDAS guidance.
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Agent E3 - Mixed Methods Integration Specialist - Qual-Quant data integration and meta-inference. Covers joint display creation, integration strategies, and legitimation techniques.
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Universal Meta-Analysis Codebook v2.2 - AI-Human collaboration for meta-analysis data extraction. 4-layer design: Identifiers, Statistics, AI Provenance, Human Verification. Integrates with C5/C6/C7 agents and Category I systematic review pipeline. Triggers: meta-analysis, codebook, data extraction, Hedges g, effect size
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections. Use when working with .canvas files, creating visual canvases, mind maps, flowcharts, or when the user mentions Canvas files in Obsidian.
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Organize messy conference LaTeX template .zip files into clean Overleaf-ready structure. Use when the user asks to "organize LaTeX template", "clean up .zip template", or "prepare Overleaf submission template".
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Diverga Dashboard - Live configuration status and feature overview. 24 specialized agents across 9 categories for social science research. VS methodology prevents mode collapse. Human checkpoints enforce human-in-the-loop decisions. Triggers: /diverga, diverga dashboard, diverga status
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
Humanization Pipeline Orchestrator v3.1 - Multi-pass 4-layer transformation pipeline Orchestrates G5 (Auditor), G6 (Humanizer), F5 (Verifier) in sequential passes Enforces checkpoints between every pass with mandatory AskUserQuestion Supports conservative (L1-2), balanced (L1-3), balanced-fast (L1-3 merged), aggressive (L1-4) modes Rich Checkpoint v2.0: section-level scores, selective humanization, target auto-stop G5+F5 parallel execution, section-selective humanization Triggers: humanize, humanize my draft, humanize manuscript, make natural, remove AI patterns Korean triggers: 휴먼화, 자연스럽게, AI 패턴 제거
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新
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
日本語の概要は準備中です。原文の説明を表示しています。
brycewang-stanford/Auto-Empirical-Research-Skills☆ 4,6622026年10月15日 更新