Skill UI

Details how to research context, structure homepage or landing copy, and follow clarity/benefit-driven rules so every headline, CTA, and section pushes visitors toward a single conversion.

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.

A skill for orchestrating parallel AI subagents that compete on the same task using isolated git worktrees. Handles session setup, agent spawning, progress monitoring, evaluation via metrics or LLM judge, and final merge of the winning branch. Ideal for code optimization, content variation, research exploration, and any task benefiting from parallel competition.

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 advanced skill acts as a hybrid router for complex research queries. It deterministically classifies the user's intent, delegating to specialized modules (e.g., funding grants, academic literature, patent search, company dossier) when applicable. If no specialist fits, it executes a robust fallback plan, synthesizing multi-source information into a detailed, cited briefing or document. Ideal for comprehensive investigative research across multiple domains.

A read-only tool designed to compare prediction-market baskets or themes against the user's existing knowledge base, portfolio notes, research memos, or financial context. It performs gap analysis, flagging potential conflicts, stale assumptions, and areas of missing research. This skill provides structured insights into fit and exposure without offering any investment or trading advice.

This guide teaches researchers how to rigorously assess a topic's geographic significance and determine its optimal fit within the Annals of the American Association of Geographers (AAG). It implements a two-part fit test—ensuring the argument is fundamentally geographic (space/place/scale are load-bearing) and possesses sufficient reach to interest neighboring subfields. It guides authors in choosing the correct area (e.g., Human, Physical, Methods) and appropriate journal venue (Annals vs. specialty). Use this when defining a project scope, overcoming reviewer critiques, or deciding wher...

This guide helps researchers strategically decide if their AI project is a strong fit for the AAAI conference. It provides detailed criteria, including strong and weak signals, and routing logic to determine whether the work should be submitted to AAAI, or routed to specialized venues like NeurIPS, ICML, or domain-specific conferences (e.g., CVPR, KDD). Use it to refine your project's framing and maximize submission success.

A comprehensive project management guide for submitting academic papers to the AAMAS conference. It details all critical milestones, including venue fit, evidence locking, abstract submission, double-blind review, rebuttal, and final camera-ready preparation. Essential for managing complex research involving game theory and multi-agent systems.

This skill guides researchers to determine if their topic fits the scope of Acta Automatica Sinica (AAS). It provides detailed boundaries for control theory, pattern recognition, robotics, and smart systems, helping authors distinguish their contribution from pure algorithms or system software, and advising on appropriate article type (Review/Paper/Short) to avoid initial rejection.

This skill is a specialized tool designed for scholars to assess the suitability and scope of their academic review article against the rigorous standards of top-tier management journals, such as Academy of Management Annals (AMA). It helps determine if a manuscript is merely a summary, or if it possesses the necessary synthesis, new organizing framework, and credible future research agenda required for publication in a highly selective venue.

This advanced skill provides a rigorous framework for assessing whether an organizational or management research manuscript is suitable for highly selective, theory-driven journals like the Academy of Management Journal (AMJ). It guides authors on necessary theoretical framing, methodological rigor (e.g., endogeneity, causal inference), structural improvements, and common pitfalls to avoid, significantly improving submission quality and maximizing acceptance chances.