c1
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
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill c1 --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
Proposes optimal quantitative designs for quantitative research problems, focusing on experimental, quasi-experimental, and survey methodologies. It provides structured steps to identify design options, assess validity and power, and outline concrete implementation plans. The output emphasizes design selection, effect sizes, sample sizing, and practical considerations, guided by a prompt template that asks for research question, causal inference need, random assignment feasibility, resources, constraints, and expected effect size.
How it works
- Identifies the most suitable research design family (true experimental, quasi-experimental, or survey) given the research question and causal-inference need.
- Presents design options with typically cited structures and methodological notes (e.g., randomization, controls, observational requirements).
- Assesses validity threats (internal, external, construct, statistical) and proposes corresponding control strategies.
- Includes power and sample size guidance using standard parameters (e.g., alpha .05, power .80) and typical effect-size benchmarks (small, medium, large).
- Outlines implementation elements: randomization procedures, data collection timepoints, and analysis plans (primary and secondary).
- Encapsulates these elements in a structured output suitable for a Quantitative Research Design Consulting Report, including a detailed power analysis section, sampling strategy, and an implementation plan.
When to use it
- When a research question requires selecting among experimental, quasi-experimental, or survey designs.
- When planning power analysis and sample size calculations.
- When developing materials, sampling strategies, and validity controls under given resource constraints.
- When a causal-inference need is High/Medium/Low and random assignment feasibility is Yes/No/Partial.
What it can touch
- Tools and parameters for power analysis and sample size planning (e.g., G*Power, pwr, statsmodels) are referenced for implementation guidance in the design recommendations.
Caveats
- Content relies on standard quantitative design principles and does not guarantee results; specific effect sizes, sample sizes, and feasibility depend on the provided inputs.
- The description does not introduce new statistical methods beyond established design options and commonly used power/sample-size conventions.
## 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("c1")` → must return `approved
What does the c1 skill do?
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
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill c1 --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.