study-design
Clarify hypotheses, variables, randomization, power, statistical tests, and reporting before data collection.
Before you start
Choose your system and agent, then run the command in your terminal. After installation, send the prompts below to your agent.
curl -sSL https://paperskills.com/scripts/paperskills-install.sh | bash -s -- \
--tool codex \
--skills topic-framing,sa.experimental-design,sa.statistical-power,sa.statistical-analysis,research-gap \
--registry https://paperskills.com/api/registryStep 1 · topic-framing
Task for your agent
Generate three testable research questions from these notes. For each, list variables, mechanism, boundaries, available data, and the minimum viable design.Expected outputs
Step 2 · sa.experimental-design
Task for your agent
Use experimental-design to design the study, covering controls, randomization/matching, blocking, confounding control, sampling rules, and failure risks.Expected outputs
Step 3 · sa.statistical-power
Task for your agent
Use statistical-power to calculate sample size, power curves, and minimum detectable effects, then provide a statistical reporting template.Expected outputs
Turn a broad interest into a researchable, publishable, executable paper topic.
View skillDesign experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger this even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.
View skillSample-size and statistical power calculations for planning studies. Use whenever someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for designs with no formula — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Use this skill even when the request only mentions an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.
View skillGuided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
View skillExtract credible research gaps and contribution space from the literature.
View skill