Skill UI

Astropy is a core Python library for astronomy and astrophysics workflows. It provides essential functionality for astronomical research and data analysis, including coordinate transformations, unit and quantity calculations, FITS file operations, cosmological calculations, precise time handling, tabular data manipulation, and astronomical image processing. Use it for implementing or debugging astronomical data analysis code.

This skill prepares and validates research-only clinical decision-support artifacts, including evidence profiles, cohort tables, and survival analysis plans. It supports aggregate or synthetic research documentation and traceability. It excludes patient care and live clinical operations, focusing on governance, privacy, and statistical evaluation for qualified human review.

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.

This powerful orchestrator guides the planning, funding, scoping, and synthesis of enterprise research across four critical domains: clinical R&D, finance, market analysis, and product usability. It acts as a central hub, routing complex inquiries to specialized sub-skills and generating structured digests with actionable insights and critical 'grill challenges' for operational rigor.

Shared orchestration engine behind the orch-* skill family, implementing a gated Research-Plan-TDD-Review-Commit pipeline with size classification, agent/command mapping, and human approval gates. Used indirectly via operation skills like orch-add-feature or orch-fix-defect; read directly when tuning shared phases or adding new operations.

This comprehensive skill guides researchers through the submission process for the Chinese Journal of Management Science. It covers the journal's core academic positioning (operations research, decision analysis, complex systems), required methodological rigor (model setting, algorithms, proofs), structural guidelines, and essential formatting checks. It helps authors assess manuscript fit and identify common rejection risks before official submission.

A comprehensive guide for authors on how to position their computational or methodological contributions when submitting to the INFORMS Journal on Computing (IJOC). It emphasizes the critical need to advance against two distinct frontiers simultaneously: the Operations Research/Mathematical Science problem domain, and the core computing methodology. Use this when reviewer feedback questions the novelty or when your work straddles OR and CS boundaries.

This guide helps researchers determine if their Operations Research (OR) or analytics findings are sufficiently computation-first for submission to the INFORMS Journal on Computing (IJOC). It provides a rigorous scope test, focusing on computational/methodological advances, measurable performance claims, and reproducibility standards, ensuring authors select the correct technical area and venue.

This guide provides detailed standards for structuring and writing manuscripts submitted to the INFORMS Journal on Computing (IJOC). It mandates a precise, methods-forward, and quantitative voice, ensuring the paper is accessible to both Operations Research (OR) and Computer Science (CS) audiences. Key elements covered include abstract structure (≤300 words, no formulas), rapid contribution framing in the introduction, and using numbered pseudocode for methods.

This skill helps authors evaluate whether their research—particularly in algorithms, optimization, or machine learning for Operations Research (OR)—meets the high computational standards required by the INFORMS Journal on Computing (IJOC). It acts as a fit/venue-selection tool, guiding authors on methodological rigor, computational evidence, and structuring their submission to maximize acceptance chances.

This guide provides comprehensive strategic advice for authors submitting manuscripts related to Artificial Intelligence, Constraint Programming, and Operations Research (AI/OR) to top-tier conferences like CPAIOR. It helps refine scope, structure, evidence standards, and narrative framing to meet highly specialized reviewer expectations, ensuring the paper is positioned as a solid hybrid optimization contribution.

A comprehensive framework designed to ensure environmental economics manuscripts meet rigorous data-availability and reproducibility standards, especially for journals like JEEM. This package guides users to document the entire research pipeline, handling complex issues like data versioning, proprietary satellite/admin data, and multi-step spatial operations by establishing a credible end-to-end reproduction path.