Skill UI

This tool analyzes ML experiment results from various paths (JSON/CSV). It computes comprehensive statistics, builds detailed comparison tables based on model types and hyperparameters, and generates actionable insights. Use it when you need to interpret raw experimental data, compare different model runs, or determine the next steps for research.

This skill automatically generates an interactive SQL Notebook designed to validate changes in dbt models or snapshots. It processes changes identified from a GitHub PR or local repository, generating comparison queries that compare 'before' (production) and 'after' (development) data states. It is essential for embedding data observability and quality checks into CI/CD pipelines.

Build, fit, and validate Bayesian models with PyMC 6, covering hierarchical models, MCMC/NUTS sampling, variational inference, prior and posterior predictive checks, diagnostics, and LOO/WAIC model comparison.

Zero-context verification that every number, comparison, and scope claim in research papers matches raw result files. Uses a fresh cross-model reviewer to prevent confirmation bias. Ensures paper-to-evidence fidelity before submission. Checks for number inflation, best-seed cherry-picking, config mismatches, aggregation errors, delta calculation mistakes, caption-table mismatches, and scope overclaims.

This skill guides researchers on the mandatory, rigorous evaluation of complex systems models (e.g., AgSy). It details best practices for model validation, emphasizing the comparison of observed vs. simulated data using independent, held-out datasets. Key components covered include standard fit statistics (RMSE, NSE), sensitivity analysis (identifying core drivers), uncertainty propagation, and multi-objective trade-off/scenario analysis to ensure scientific credibility.

This guide outlines the rigorous, model-based standards required for data analysis and statistical reporting in Cognitive Psychology manuscripts. It covers essential techniques such as model comparison (using AIC/BIC or Bayes factors), demonstrating parameter and model recovery, implementing mixed or hierarchical Bayesian models, and ensuring all findings are fully reproducible from shared code.

This guide provides a rigorous strategy for authors responding to major or minor revisions in cognitive psychology journals. It teaches how to systematically address every reviewer comment, strengthen model-driven inferences through techniques like model comparison and recovery analysis, and maintain the coherence and reproducibility of the theoretical argument. Essential reading for scholarly publication.

This comprehensive checklist guides authors through the final pre-submission review process for demanding academic journals. It ensures the manuscript is scientifically rigorous and complete, focusing on critical areas such as model-driven theoretical advancement, model comparison, data reproducibility (depositing code and verifying fit regeneration), and adherence to open science mandates. Use this guide before uploading to the journal's portal.

Provides detailed guidelines for developing tables and figures in Developmental Psychology manuscripts, adhering to APA 7th edition standards. Focuses on making developmental change explicit by using age/time on the x-axis, plotting individual trajectories (spaghetti plots), and displaying model-implied mean curves with confidence intervals, moving beyond simple group mean comparisons.

Provides rigorous guidelines for designing complex quantitative studies, including cross-national comparisons, longitudinal panels, and multilevel models. It emphasizes identifying mechanisms, assessing measurement equivalence, and defending causal claims against confounders in advanced sociological research.

A comprehensive guide for designing and stress-testing asset-pricing empirical models, tailored for submissions to top-tier finance journals. Covers methodologies like Fama–MacBeth regressions, factor model comparisons, handling standard-error subtleties, and rigorous out-of-sample validation. Essential for ensuring results survive academic scrutiny, addressing issues like multiple testing and economic magnitude.

This guide establishes rigorous methodological standards for incorporating forecasting and prediction claims in academic manuscripts. It emphasizes the necessity of genuine out-of-sample evaluation, comparison against robust benchmarks (like Random Walk or AR models), and utilizing advanced statistical tests (e.g., Diebold-Mariano) to prove prediction superiority, moving beyond simple in-sample fit reporting.