Skill UI

This skill provides a comprehensive guide for authors targeting The Cryosphere journal. It details the journal's scope (ice sheets, permafrost, sea ice), high methodological standards (FAIR data, uncertainty quantification), and the unique EGU interactive public peer-review process. Use this tool to determine if a cryospheric study is suitable for this specific open-access venue.

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 comprehensive guide for authors submitting to UAI (Uncertainty in Artificial Intelligence). It details how to position research by ensuring coverage across diverse academic communities, including Machine Learning, Statistics, Causal Inference, and foundational mathematics. Focuses on establishing the technical 'delta' rather than merely listing citations, helping authors avoid common review failures.

This guide helps researchers determine the most appropriate conference (UAI, AISTATS, NeurIPS, ICML, etc.) for their work, particularly when the core contribution revolves around uncertainty representation, probabilistic reasoning, or causal inference. It provides detailed filters and comparison points to refine the paper's scope and target audience.

Track physical units and propagate measurement uncertainty in scientific Python calculations using pint and uncertainties, from unit conversion and GUM budgets to Monte Carlo and dimensional sanity checks.

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.

This skill provides comprehensive norms for executing and reporting robust, reproducible data analysis in comparative, cross-national, or formal-empirical studies. It guides authors on honestly reporting uncertainty, ensuring statistical robustness, performing rigorous cross-national inference, and integrating qualitative evidence through triangulation to meet the demanding standards of top academic journals.

This skill guides the creation of self-contained, highly accessible, and reproducible exhibits (tables, figures, maps) for World Politics manuscripts. It addresses strict length limits by differentiating between core arguments (in the article) and supplementary material (online appendix). It emphasizes visual communication excellence, moving beyond simple coefficient tables to forest plots and event studies to convey magnitude and uncertainty effectively.

This skill enhances raw clinical entity recognition by assigning three critical axes: Negation, Temporality, and Uncertainty (ConText). It determines the clinical status of a mention (e.g., affirmed, negated, historical, or uncertain), ensuring that basic NER results are transformed into clinically faithful assertions before grounding to standards like FHIR. Essential for accurate problem list generation and differential diagnosis support.

A strategic framework for making decisions when facing extreme uncertainty and unclear outcomes. Effectuation instructs users to start with available means (skills, assets, network) and define an affordable loss limit. Progress is driven by taking small, controlled actions to gain real commitments and information, allowing the ultimate goal to emerge naturally.

This methodology provides a comprehensive framework for estimating buffered numbers—such as capacity limits, timeframes, or budgets—when making commitments under uncertainty. It guides users to calculate a buffer based on both residual estimation error and the potential cost of failure (breach cost), ensuring resilience against costly underestimation in critical systems or plans.

A rigorous framework for making evidence-based estimates and forecasts when precise data is unavailable. It guides users to anchor predictions on base rates, articulate uncertainty using confidence intervals, and systematically update beliefs using likelihood ratios (prior to posterior) as new evidence arrives. Ideal for risk sizing, project timelines, and strategic decision-making.