skills/25-HosungYou-Diverga/skills/setup/SKILL.md
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
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research setupInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
When user invokes /diverga:setup, execute this interactive wizard:
Check for existing config:
~/.claude/plugins/diverga/config/diverga-config.json exists with researcher field → "Existing profile detected. Update?"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:
Doctoral Student → A1 explains PICO/SPIDER, C1 scaffolds power analysis, E1 adds interpretation guidanceFaculty → Agents skip explanations, go straight to options and trade-offsAsk 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:
SCH_DATABASE_SELECTION checkpointquestion: "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:
~/.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_critique": false }
}
If "Disabled" selected, add to config:
{
"agent_teams": { "enabled": false },
"vs_arena": { "enabled": false }
}
After collecting all preferences, generate config/diverga-config.json at ~/.claude/plugins/diverga/config/:
{
"version": "12.0.0",
"researcher": {
"discipline": "Education",
"experience": "doctoral_student",
"stats_software": ["R", "SPSS"],
"db_access": ["Scopus", "ERIC", "Semantic Scholar"]
},
"agent_teams": { "enabled": true },
"vs_arena": { "enabled": true, "team_size": 3, "cross_critique": true }
}
Experience level mapping:
"doctoral_student""early_career""faculty"Display completion:
╔══════════════════════════════════════════════════════════════════╗
║ Diverga v12.0 Setup Complete! ║
╠══════════════════════════════════════════════════════════════════╣
║ ║
║ Profile saved: ║
║ Discipline: Education ║
║ Experience: Doctoral Student ║
║ Stats: R, SPSS ║
║ Databases: Scopus, ERIC, Semantic Scholar ║
║ Agent Teams: Enabled ║
║ VS Arena: Cross-critique enabled ║
║ ║
║ 29 agents (24 core + 5 VS Arena) ║
║ Agent Teams orchestrator — unified parallel and debate workflows║
║ ║
║ Quick Start: ║
║ - Just describe your research in natural language ║
║ - "I want to conduct a systematic review on AI in education" ║
║ - Diverga adapts to your profile automatically ║
║ ║
║ Commands: ║
║ - /diverga:help - View all 29 agents ║
║ - /diverga:memory - Memory system commands ║
║ - /diverga:setup - Update your profile anytime ║
║ ║
╚══════════════════════════════════════════════════════════════════╝
These settings are NOT asked during setup. Each agent asks when first needed:
| Setting | Agent | When Asked | Storage |
|---------|-------|------------|---------|
| OpenAlex email | G1 (Journal Matcher) | First journal_search_by_field call | .omc/config.json |
| Citation format | G2 (Publication) | First manuscript generation | researcher.citation_format in config |
| Qualitative software | E2 (Coding) | First qualitative coding task | researcher.qual_software in config |
Lazy config prompt template (for agents to use):
When an agent needs a lazy config value that isn't set yet:
config/diverga-config.json under the researcher objectExample (G1 first call):
"Journal search uses OpenAlex API. Registering your email enables
faster responses (polite pool). This is optional."
→ AskUserQuestion: [Enter email] [Skip]
→ If email: save to .omc/config.json
When a Diverga plugin session starts:
~/.claude/plugins/diverga/config/diverga-config.json exists with researcher fieldWelcome to Diverga! Run /diverga:setup to set up your researcher profile (1 minute).
{
"version": "12.0.0",
"researcher": {
"discipline": "string",
"experience": "doctoral_student | early_career | faculty",
"stats_software": ["R", "Python", "SPSS", "Stata", "Mplus"],
"db_access": ["Scopus", "Web of Science", "PsycINFO", "ERIC", "Semantic Scholar"],
"qual_software": "NVivo | ATLAS.ti | MAXQDA | Dedoose | manual",
"citation_format": "APA | Chicago | Vancouver | Harvard"
},
"agent_teams": {
"enabled": "boolean"
},
"vs_arena": {
"enabled": "boolean",
"team_size": "number (default 3)",
"cross_critique": "boolean"
}
}
Fields are added incrementally:
researcher.discipline, experience, stats_software, db_access — set during /diverga:setupresearcher.qual_software, citation_format — added by lazy config when relevant agent runsagent_teams, vs_arena — set during /diverga:setup Step 3If config directory doesn't exist, create it:
mkdir -p ~/.claude/plugins/diverga/config
development
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.