Skill UI

Astropy is a Python library for astronomical research that handles unit-aware quantities, celestial coordinates, FITS files, cosmological models, precise time systems, and tabular data, letting you transform, convert, and analyze observational or theoretical datasets.

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.

GeoMaster is a comprehensive geospatial science skill that guides remote sensing, GIS, spatial analysis, and machine learning workflows for Earth observation and planetary data projects, covering satellite imagery, vector/raster math, point clouds, terrain, hydrology, and cloud-native data stacks.

Anndata is a Python package for handling annotated data matrices, storing experimental measurements alongside observation and variable metadata. It supports single-cell genomics analysis, managing h5ad and Zarr files efficiently. Users can read, write, concatenate, and subset data with sparse matrix support. It integrates with the scverse ecosystem including Scanpy and scvi-tools. Ideal for biological data workflows requiring efficient storage and manipulation.

Provides geospatial research workflows for remote sensing, vector and raster GIS, spatial statistics, terrain and network analysis, and Earth-observation machine learning across Python, R, Julia, and other languages.

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 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 guide outlines rigorous standards for conducting heterogeneity analysis in economic research. It stresses that heterogeneity should not merely involve slicing data by region or size, but must be theoretically driven, linking multiple dimensions (e.g., ownership, financing constraints, industry type) to core economic mechanisms. The analysis must demonstrate significant group differences and clearly connect findings to differential policy implications, elevating the discussion from mere observation to actionable policy science.

A comprehensive skill designed for authors targeting Classical and Quantum Gravity (CQG). It guides users on the journal's specific scope, methodological rigor (including mathematical proofs, numerical analysis, and observational constraints), and appropriate academic framing for manuscripts in general relativity, cosmology, and quantum gravity. Use this to assess article suitability before submission.

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.

A comprehensive guide for ensuring rigorous statistical inference in experimental economics. It covers critical areas such as power justification, determining the correct unit of observation (cluster-robust SEs), handling multiple comparisons, and applying non-parametric tests. This framework helps researchers transition from preliminary findings to statistically robust, publishable conclusions.