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Found 27 Skills
Assumption mapping and product hypothesis testing frameworks for validating product ideas.
Probability, distributions, hypothesis testing, and statistical inference. Use for A/B testing, experimental design, or statistical validation.
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.
Perform statistical hypothesis testing, regression analysis, ANOVA, and t-tests with plain-English interpretations and visualizations.
Ascend C Code Inspection Skill. Conduct security specification inspection on code based on the hypothesis testing methodology. When calling, you must clearly provide: code snippets and inspection rule descriptions. TRIGGER when: Users request code inspection, code review, ask code security questions, check coding specifications, or need to check specific code issues (such as memory leaks, integer overflows, null pointers, etc.). Keywords: Ascend C, code inspection, code review, security specification, memory, pointer, overflow, leak, coding specification.
Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Use when you need to validate whether observed differences are real, size an experiment correctly before launch, or interpret test results with confidence.
Select the right Proof of Life (PoL) probe based on hypothesis, risk, and resources. Use this to match the validation method to the real learning goal, not tooling comfort.
Generate a Lean Canvas with problem, solution, metrics, cost structure, UVP, unfair advantage, channels, segments, and revenue. Use when exploring a lean startup canvas, testing a business hypothesis, or modeling a new venture.
Statistical analysis: t-tests, chi-squared, Mann-Whitney, p-values, CIs, Bonferroni/BH, Bayesian A/B
Defines a testable hypothesis with clear success metrics and validation approach. Use when forming assumptions to test, designing experiments, or aligning team on what success looks like.
10 statistical analysis skills. Trigger: statistical tests, Bayesian analysis, hypothesis testing, sampling. Design: method guides covering assumptions, code, and result interpretation.
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?"