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Found 78 Skills
Эксперт анализа распределений. Используй для statistical distributions, data analysis и hypothesis testing.
Deep analysis debugging mode for complex issues. Activates methodical investigation protocol with evidence gathering, hypothesis testing, and rigorous verification. Use when standard troubleshooting fails or when issues require systematic root cause analysis.
Use this skill when performing exploratory data analysis, statistical testing, data visualization, or building predictive models. Triggers on EDA, pandas, matplotlib, seaborn, hypothesis testing, A/B test analysis, correlation, regression, feature engineering, and any task requiring data analysis or statistical inference.
Core consulting thinking frameworks and methodologies for structuring business problems, communicating findings, analyzing strategy, building financial models, and designing operations. Use when any agent or command needs to apply MECE decomposition, pyramid principle, hypothesis-driven analysis, issue trees, SCR communication, Porter's Five Forces, TAM/SAM/SOM market sizing, value chain analysis, NPV/IRR decision criteria, build/buy/partner evaluation, RACI matrices, or any standard consulting framework. This skill provides procedural guidance — not just framework names, but how to apply them correctly.
Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.
Use when developing or documenting trading strategies - guides edge hypothesis formation, validates statistical significance, documents strategy rules systematically (entry, exit, risk management). Activates when user says "research this strategy", "document my approach", "test this idea", mentions "trading strategy", "edge", or uses /trading:research command.
Systematic methodology for debugging bugs, test failures, and unexpected behavior. Use when encountering any technical issue before proposing fixes. Covers root cause investigation, pattern analysis, hypothesis testing, and fix implementation. Use ESPECIALLY when under time pressure, "just one quick fix" seems obvious, or you've already tried multiple fixes. NOT for exploratory code reading.
When the user wants to design, prioritize, or analyze growth experiments -- including A/B tests, hypothesis frameworks, ICE/RICE scoring, or growth sprints. Also use when the user says "A/B test," "experiment design," "growth sprint," "experiment prioritization," or "statistical significance." For analytics setup, see product-analytics. For growth modeling, see growth-modeling.
Assumption mapping and product hypothesis testing frameworks for validating product ideas.
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
Design and generate property-based tests (PBT) for changed files in the current git branch. Extracts specifications, designs properties (invariants, round-trip, idempotence, metamorphic, monotonicity, reference model), builds generator strategies, implements tests, and self-scores against a rubric (24/30+ required). Supports fast-check (TS/JS), hypothesis (Python), and proptest (Rust). Use when: (1) "write property tests for my changes", (2) "add PBT", (3) "property-based test", (4) after implementing pure functions, validators, parsers, or formatters to verify invariants.
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.