Skill UI

Provides a Python client for querying the Data Commons knowledge graph so teams can resolve entities, fetch statistical observations, and explore public demographic, economic, and health datasets with optional Pandas support.

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.

This skill explains complex concepts using the Socratic method. Instead of delivering direct answers or textbook definitions, it engages the user in a reflective, conversational dialogue. By guiding the user through thought experiments, small observations, and simple analogies, the assistant helps the user construct understanding step-by-step, facilitating deeper clarity through active reasoning. Ideal for understanding theories, abstract ideas, or system mechanics.

This skill helps formulate evidence-bounded scientific questions and candidate hypotheses. It turns observations into transparent, testable research plans without treating hypotheses as facts. It distinguishes between observations, mechanisms, and predictions, ensuring rigorous research design and safety compliance.

This skill provides a rigorous, operational framework for product and user research. It helps researchers select the appropriate study method (generative, evaluative, validation) based on goals and product stages. It also calculates defensible sample sizes and saturation levels, and synthesizes raw observations into governed, evidence-based insights, ensuring all findings are rooted in cross-participant recurrence.

Transforms complex raw product metrics into a clear, actionable health narrative. This skill analyzes key performance indicators (KPIs) across the entire user lifecycle—including acquisition, activation, engagement, and retention—against set OKRs and benchmarks. The output is a comprehensive report providing overall health status, key observations, hypothesized root causes for performance dips, and prioritized, actionable next steps with assigned owners.

This skill guides authors in assessing the suitability of their research concerning agricultural or forest ecosystems for journals focused on land–atmosphere interactions. It emphasizes that the core contribution must be a biophysical process—such as the exchange of energy, water, or carbon—rather than purely agronomic or ecological observations. Use it to re-frame your work to highlight micrometeorological processes and robust flux measurement methodologies.

A comprehensive guide for structuring and defending the methodological design of social science manuscripts, particularly for high-impact journals like AJS. It covers diverse traditions—including quantitative, comparative-historical, ethnographic, and network analysis—ensuring the design rigorously supports the theoretical claim, rather than just presenting data. Use this when justifying case selection, identifying assumptions, or linking field observations to major theoretical arguments.

This skill guides the transformation of rich fieldwork data, material evidence, or linguistic observations into portable, theoretical arguments suitable for high-level anthropological publishing. It structures the conceptual framework, defining core concepts, interpretive mechanisms, and scope conditions, ensuring the contribution moves beyond mere description to generate cross-subfield theoretical insights.

This skill guides the transformation of visual observations and documentary evidence into a sustained, contestable academic thesis, suitable for high-level scholarly publication. It teaches how to explicitly state a claim (the thesis), define its scholarly stakes, and rigorously warrant every interpretive leap using evidence chains and anticipating counter-readings. Use this when a draft is descriptive but lacks a clear, disputed argument.

This skill guides the transformation of preliminary observations into high-stakes, original academic interventions. It helps users sharpen the central thesis of critical essays, defining the argument's scope, identifying the profound 'so what?' for the discipline, and ensuring the claim is bold, transformative, and defensible through evidence and theory.

This guide helps transform rich but purely descriptive fieldwork, archaeological data, or linguistic observations into bold, transferable, and debatable theoretical arguments. It teaches how to explicitly define concepts, establish interpretive mechanisms, set scope conditions, and incorporate positionality, ensuring the work is robust enough to withstand academic critique.