c3
Agent C3 - Mixed Methods Design Consultant Comprehensive mixed methods research design specialist covering sequential, concurrent, embedded, and multiphase designs with Morse notation. Core Capabilities: - Sequential Explanatory (QUAN → qual): Explain quantitative results - Sequential Exploratory (QUAL → quan): Develop instruments - Convergent Parallel (QUAN + QUAL): Comprehensive understanding - Embedded (QUAN(qual)): Secondary strand addresses different question - Multiphase: Long-term projects with iterative phases - Morse notation interpretation and recommendation
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill c3 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Agent C3 acts as a Mixed Methods Design Consultant, providing expert guidance on choosing and applying mixed methods designs (Sequential Explanatory QUAN → qual, Sequential Exploratory QUAL → quan, Convergent Parallel QUAN + QUAL, Embedded QUAN(qual) or QUAL(quan), and Multiphase). It outlines the Morse notation for each design, specifies structure, priority, timing, and integration points, and offers design workflows, examples, and when-to-use conditions.
How it works
The skill presents each design type with: Morse notation, structure diagrams, priority, timing, integration point, When to Use criteria, example studies, and a step-by-step design workflow (phases, data collection, analysis, and integration). It also provides a Design Selection Flowchart (Step 1–4) to help pick a design based on purpose, priority, timing, and integration method, plus a Morse Notation Guide and Integration Strategies (Connecting, Merging, Embedding, Transforming).
When to use it
Activate when you need to combine qualitative and quantitative methods for a research question, require multiple data types, or need to explain, develop, or triangulate findings. Triggers include requests containing keywords like mixed methods design, 순차적, sequential, 동시적, concurrent, convergent, QUAL-quan, quan-QUAL, and related cues that indicate a mixed methods need.
What it can touch
The skill references designs, workflows, and integration methods; it does not specify executable tools beyond describing mixed methods processes. It mentions no external files or commands to run, and there is no list of required software tools beyond the conceptual methods framework.
Caveats
The content is descriptive of study designs and workflows, including Morse notation and integration strategies. It does not guarantee outcomes, and approval by a human researcher (CP_METHODOLOGY_APPROVAL) is noted as a prerequisite for methodology selection.
## VS Arena Check (v11.1) Before proceeding with internal VS, check if VS Arena is enabled: 1. Read `config/diverga-config.json` → `vs_arena.enabled` 2. If `true` → delegate to `/diverga:vs-arena` instead of internal VS process 3. If `false` or config unavailable → proceed with internal VS below ## ⛔ Prerequisites (v8.2 — MCP Enforcement) `diverga_check_prerequisites("c3")` → must return `approved
What does the c3 skill do?
Agent C3 - Mixed Methods Design Consultant Comprehensive mixed methods research design specialist covering sequential, concurrent, embedded, and multiphase designs with Morse notation. Core Capabilities: - Sequential Explanatory (QUAN → qual): Explain quantitative results - Sequential Exploratory (QUAL → quan): Develop instruments - Convergent Parallel (QUAN + QUAL): Comprehensive understanding - Embedded (QUAN(qual)): Secondary strand addresses different question - Multiphase: Long-term projects with iterative phases - Morse notation interpretation and recommendation
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill c3 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.