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Found 29 Skills
Guided 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.
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?"
Guide product managers through Jeff Gothelf's Lean UX Canvas v2—a one-page tool that frames work around a business problem, exposes assumptions, and ensures learning every sprint.
Facilitates the third step of a proven customer-interview method: translating hypotheses into open-ended, unbiased interview questions — each a miniature experiment designed to test one hypothesis without leading the witness. Two modes: given a single hypothesis, it grills the question into shape and outputs the final question in chat; given a HYPOTHESES.md file, it iterates the whole list, grouping related hypotheses, and maintains a QUESTIONS.md file (numbered Q1, Q2, … mapped to H-numbers) as a live, resumable artifact. Load when the user has hypotheses and wants interview questions, asks how to phrase a question for customers without biasing the answer, or says 'turn my hypotheses into questions' or 'help me ask about X without leading.' Do NOT load for writing goal questions or hypotheses (earlier steps), for conducting or analyzing the interviews themselves, for survey/questionnaire design, or for job interviews.
Senior AI Product Manager. Expert in Probabilistic Strategy, Rapid Agentic Prototyping, and Hypothesis Generation for 2026.
Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.
Perform statistical hypothesis testing, regression analysis, ANOVA, and t-tests with plain-English interpretations and visualizations.
Help users de-risk new product bets by testing core assumptions, building low-fidelity prototypes, and gathering high-signal evidence before investing heavy engineering resources.
Use when asked to calculate statistical power, determine sample size, or plan experiments for hypothesis testing.
부트캠프 4단계로, TourAPI 데이터를 받아 팀 가설을 단순분석으로 검증한다. 이 스킬은 사용자가 직접 호출할 때만 사용한다. 일반 대화에서는 자동으로 발동하지 않는다.
Define a Proof of Life (PoL) probe—a lightweight validation artifact that surfaces harsh truths before expensive development. Use it to test hypotheses with minimal investment.
Comprehensive statistical analysis for research, experiments, and data science. Covers hypothesis testing, effect sizes, confidence intervals, Bayesian methods, regression, and advanced techniques. Emphasizes correct interpretation and avoiding common statistical mistakes. Use when ", " mentioned.