Skill UI

This skill guides authors on submitting original, high-quality, and reusable Earth System datasets (observations, reanalysis, model outputs) as primary publications. It assesses whether the work fits the criteria of a 'data paper,' requiring open deposition in a FAIR repository with a persistent DOI, detailed provenance documentation, and rigorous quality control. It serves as a venue-selection and manuscript re-framing tool.

This guide assists researchers in transforming vague datasets or observations into robust, positive, and economics-driven research questions suitable for prestigious accounting journals (like JAE). It teaches key concepts, such as identifying economic friction, distinguishing between normative and positive questions, establishing plausible identification strategies, and mapping the topic to specific academic domains.

This comprehensive guide helps academics transform raw financial data observations into rigorous and high-impact research questions suitable for top-tier finance journals. It teaches a structured methodology involving anchoring the core financial mechanism, integrating specific institutional contexts within China, and defining a clear incremental academic contribution.

A comprehensive methodology guide for qualitative researchers in sociology. It teaches how to move beyond mere description by systematically extracting, defining, and bounding core concepts from empirical field materials. This process involves iterative dialogue between observations and existing sociological theory, ensuring the concept is theoretically robust and transferable.

This comprehensive guide helps researchers defend their complex study designs—whether they involve lab experiments, large-scale surveys, or qualitative observations—against rigorous methodological critique. It emphasizes establishing a clear, credible link between theoretical mechanisms and empirical evidence, focusing specifically on the structure-individual link. It covers advanced inference techniques, identifying potential confounding variables, and articulating the design's limitations.

This skill processes raw, report-only optimization observations generated by Caveman tools. It guides the user through an evidence-first, rigorous evaluation process, requiring explicit operator choice, designing a minimal candidate change, and performing a paired baseline evaluation using identical fixed inputs. It ensures that any proposed code change is validated against quality checks and established performance metrics before implementation.

A comprehensive guide and set of rules for writing effective, warm, and actionable comments across various development platforms. It helps users structure feedback for GitHub PRs, issue trackers, or Slack messages, ensuring the tone is consistently thoughtful, direct, and focused on clear next steps, rather than simply listing observations.

A set of behavioral guidelines derived from Andrej Karpathy's observations to help users writing, reviewing, or refactoring code with Large Language Models (LLMs). It guides developers to avoid overcomplication, make surgical changes, explicitly surface assumptions, and define verifiable success criteria, promoting clean, simple, and robust code development.

Analyze weather radar and satellite observations as remote-sensing data: validate product metadata, geometry and calibration, derive storm and cloud structures, track their evolution, and quantify uncertainty for short-term nowcasting.

Retrieve surface and upper-air weather observations from NOAA APIs and archives, preserving station identity, time, units, and quality flags. Covers METARs, ISD, IGRA radiosondes, and station metadata for analysis-ready data.

Reviews pasted text to detect signs of AI-generated content, returning an indeterminate authorship status with quoted editorial observations and suggested wording improvements. Useful for SEO content audits, editing, and quality review.