Skill UI

Enables Claude to analyze historical time series patterns, select the best forecasting model, and predict future values with confidence intervals for sales, traffic, or other time-dependent metrics.

This technique guides users to use precise chemical compound names, specific concentration values (e.g., "1.5 mg/L"), and taxonomic synonym counts as exact-match search anchors when querying biological literature. By leveraging quotation marks and combining multiple specific terms, it uniquely identifies research papers or species details, greatly improving search precision in biochemistry and botany.

This skill provides bounded, local exploratory data analysis for diverse scientific data files, including CSV, JSON, HDF5, and image metadata. It rigorously audits data structure, detects missing values, and identifies outliers in a secure, sandboxed environment. It is designed exclusively for pre-modeling data integrity checks and must not be used to infer scientific meaning or make causal claims.

SHAP (SHapley Additive exPlanations) is a powerful technique used to interpret and audit machine learning model predictions. It calculates feature attributions (SHAP values) to explain how input features map to model outputs. This skill guides users through selecting appropriate explainers, handling multi-output models, validating feature attributions, and generating visualizations for both local and global model understanding.

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.

This skill extracts precise, pixel-level data from various chart types (bar, line, scatter, pie) and graphs presented in images. It generates a comprehensive structured data table, including confidence levels, axis details, and assumptions, making the raw data ready for spreadsheets or further analysis. Ideal for digitizing charts, transcribing graph values, or turning visual data into actionable formats.

This tool performs robust outlier detection on individual session spending. Utilizing Median and Median Absolute Deviation (MAD), it calculates a modified z-score, making it highly resistant to extreme values, unlike mean+sigma methods. It specifically identifies which singular sessions are anomalous, making it crucial for cost monitoring, auditing, and integrating into CI/CD pipelines to prevent unexpected resource overruns.

A comprehensive guide and checklist for ensuring that figures and tables in medical manuscripts comply with the rigorous reporting standards of JAMA. It covers mandatory elements such as CONSORT flow diagrams, reporting effect sizes with 95% CIs, structuring baseline characteristics, and avoiding statistical misrepresentation (e.g., using p-values instead of CIs).

A comprehensive guide for authors preparing medical manuscripts for high-impact journals. It outlines rigorous standards for statistical reporting, emphasizing the use of effect sizes with 95% Confidence Intervals (CIs) over p-values alone. Key topics include pre-specification, intention-to-treat analysis, multiplicity control, handling missing data through imputation, and reporting both relative and absolute effects to ensure maximum statistical rigor and reproducibility.

A comprehensive guide for advanced statistical analysis in consumer psychology, moving beyond simple p-values. It details how to rigorously test complex processes like mediation and moderation (measured and experimental). This resource emphasizes reporting standards for high-impact journals, covering effect sizes, confidence intervals, pre-registration, and establishing causal evidence.

A comprehensive guide for conducting and reporting rigorous statistical analysis for academic marketing manuscripts, particularly tailored for the Journal of Marketing (JM) standards. It covers selecting appropriate estimators for various designs (e.g., panel, choice, field experiments), managing identification, testing robustness, and adhering to the mandatory reporting of p-values, standard errors, and managerial effect sizes. Also covers data replication and advanced econometric techniques.

Provides comprehensive guidelines for building high-quality, statistically rigorous tables and figures for Journal of Marketing (JM) submissions. It emphasizes adhering to AMA house style, ensuring results display exact p-values, standard errors, and interpretable effect sizes, while strictly managing the 50-page manuscript budget.