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Found 8 Skills
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
Use when asked to calculate statistical power, determine sample size, or plan experiments for hypothesis testing.
Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.
Expert-level research methodology, academic writing, statistical analysis, and scientific investigation
Design and analyze A/B tests with proper statistical methodology including sample size calculation, randomization, frequentist and Bayesian approaches, and sequential testing. Use this skill when the user needs to set up an experiment, calculate required sample size, interpret test results, or decide between testing methodologies — even if they say 'should we A/B test this', 'how many users do we need', 'is the test result conclusive', or 'can we stop the test early'.
Design and analyze factorial experiments to identify significant process factors and optimize settings. Use this skill when the user needs to systematically test factor effects, optimize a manufacturing process, or determine which variables matter most — even if they say 'which factors affect quality', 'optimize process settings', or 'design an experiment'.
Use when investigating why something happened and need to distinguish correlation from causation, identify root causes vs symptoms, test competing hypotheses, control for confounding variables, or design experiments to validate causal claims. Invoke when debugging systems, analyzing failures, researching health outcomes, evaluating policy impacts, or when user mentions root cause, causal chain, confounding, spurious correlation, or asks "why did this really happen?"
Use when asked to "run an A/B test", "design an experiment", "check statistical significance", "trust our results", "avoid false positives", or "experiment guardrails". Helps design, run, and interpret controlled experiments correctly. Based on Ronny Kohavi's framework from "Trustworthy Online Controlled Experiments".