Skill UI

This skill enhances research query formulation by translating vague, colloquial, or generalized descriptions into precise, standard academic terminology. When initial search terms are broad (e.g., "conjugal happiness"), this technique suggests multiple plausible academic synonyms (e.g., "marital satisfaction," "relationship quality"). By improving the precision of the search query and incorporating structured constraints (like keywords, years, or sample sizes), it significantly boosts the relevance and effectiveness of academic paper retrieval.

This advanced research technique is designed for deep dives into football topics. Instead of relying solely on the original query, it mandates tracking every discovered entity—such as player names, club names, or coaches—from search results. By treating these entities as new leads, the user must formulate follow-up searches using the discovered names and remaining constraints, ensuring nothing relevant is overlooked and building a comprehensive knowledge chain.

A comprehensive mentor for graduate students and early-career researchers, guiding the entire research lifecycle. This skill covers formulating rigorous research questions, designing studies (quantitative, qualitative, mixed-methods), conducting literature reviews, analyzing data, and writing publishable papers. It emphasizes methodological rigor, critical thinking, and ethical considerations.

A comprehensive framework for transforming raw observations into rigorous, evidence-bounded, and testable research plans. It guides users through establishing research questions (PICO/PECO), formulating candidate hypotheses and rival explanations, defining causal estimands, and creating preregistration-ready analysis plans, all while maintaining strict ethical and scientific guardrails.

PyDESeq2 is a robust Python implementation of DESeq2, designed for comprehensive differential expression analysis using bulk RNA-seq count data. It supports complex formulaic designs, performs Wald tests with multiple testing correction (FDR), and includes advanced features like LFC shrinkage, enabling researchers to analyze gene expression changes between various experimental conditions within Python pipelines.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.

This structured mode acts as an experiment design partner for Product Managers. It guides users through formulating testable, falsifiable hypotheses, designing rigorous A/B tests (including sample size, duration, and guardrails), and interpreting results honestly. It helps PMs move from vague beliefs to clear, data-backed decisions: Ship, Iterate, or Kill.

This domain-agnostic skill ranks impediments, risks, and gaps by using a specialized value-stream scoring model. It evaluates countermeasures based on four criteria: Return on Investment (ROI), Cost to Implement, Ease of Deployment, and Risk Factor. A proprietary formula calculates a final Priority score (1-100), allowing users to quickly determine the optimal sequence for remediation, improvement backlogs, or triage items.

A systematic, multi-phase guide for tackling hard bugs and performance regressions. It mandates building a robust feedback loop (e.g., failing tests, harnesses), formulating falsifiable hypotheses, targeted instrumentation, and rigorous regression testing. This methodology ensures that debugging efforts are structured, efficient, and highly reproducible, making it ideal for complex software defects.

This skill details advanced techniques for exploiting formula injection vulnerabilities within CSV, Excel, LibreOffice, and Google Sheets environments. It covers DDE, obfuscation methods, and high-risk functions like IMPORT* that can be triggered when user-controlled data is exported or imported into spreadsheet software. Use this for penetration testing of data pipelines and export features.

This guide provides a comprehensive framework for designing statistically rigorous A/B tests, split tests, and multivariate experiments. It covers everything from formulating strong hypotheses and calculating sample sizes to selecting appropriate primary, secondary, and guardrail metrics for various business contexts.

A comprehensive skill for creating, editing, and analyzing complex spreadsheets (.xlsx, .csv, etc.). This skill excels in financial modeling, ensuring all calculations are managed via dynamic Excel formulas rather than hardcoded values. It supports data analysis using pandas, rigorous formatting standards, and complex formula integrity checks, making it ideal for financial reporting and data visualization.