Skill UI

This guide provides rigorous structural and stylistic guidelines for academic manuscripts in the field of risk and uncertainty. It instructs authors on how to precisely articulate decision models, quantify results with appropriate magnitude and precision, and structure arguments (Abstract, Introduction, Exposition) to meet the high standards of specialized journals, ensuring technical honesty and clarity.

This skill guides the planning and auditing of analysis for linguistic manuscripts to ensure evidence supports theoretical claims. It covers quantitative modeling like mixed-effects models in R, phonetic measurement, and corpus statistics. It helps establish the analytic trail from raw data to generalization without fabricating results. It ensures proper statistical specification, effect size reporting, and handling of uncertainty, aligning with academic standards for credibility in language research.

A guide for developing rigorous analytical or stochastic models for Manufacturing & Service Operations Management (M&SOM). It emphasizes translating operational decisions, objectives, and uncertainties into explicit structural results, such as policy forms (e.g., order-up-to levels) and comparative statics, ensuring the core insight remains operational rather than merely descriptive.

A comprehensive guide for executing and reporting complex social psychological analyses required for expert peer review. This skill covers norms for reporting uncertainty, assessing measurement quality, ensuring robustness checks, selecting appropriate statistical inference (e.g., multilevel models, clustering), and linking statistical findings back to the theoretical structure. It emphasizes methodological rigor and reproducibility.

A comprehensive guide for designing, auditing, and reporting advanced statistical and causal inference experiments. It emphasizes moving beyond simple accuracy metrics to quantify uncertainty, model robustness, and causal mechanisms. Covers metrics like coverage, KL divergence, and regret, and outlines best practices for seeding, compute fairness, and creating standardized reporting blocks (e.g., UAI standards).

A specialized guide for authors aiming to submit to the Conference on Uncertainty in Artificial Intelligence (UAI). It provides expert advice on framing the core contribution—such as probabilistic reasoning, causal inference, and uncertainty modeling—and calibrating the manuscript for highly technical, statistical ML reviewers. Use this skill to refine your submission strategy, identify evidence gaps, and properly position your work relative to other top-tier AI venues like AISTATS and IJCAI.

An anxiety-aware, evidence-driven collaboration mode for stalled or high-stakes work. It preserves the user's ideal, finds leverage, and turns grounded perspective-taking into bounded problem solving, without therapy or false certainty.

This skill creates custom Semgrep rules for detecting security vulnerabilities, bug patterns, and code patterns. It guides users through a strict workflow including test-first development, AST analysis, and validation. Ideal for building static analysis detections, enforcing coding standards, and identifying data flow issues using taint mode.