Skill UI

This skill acts as an orchestrator for developing and submitting academic manuscripts targeting PMLA. It guides authors through the entire scholarly lifecycle, helping them determine the appropriate stage (e.g., argument development, theoretical framing, textual evidence) and the best venue (e.g., regular article, special feature). It ensures the work adheres to literary and language studies standards, emphasizing close reading and scholarly positioning.

This skill refines academic prose specifically for publications like PMLA. It ensures that sophisticated theoretical arguments are presented with exceptional clarity and conciseness, making the writing highly accessible to a general membership of language and literature scholars, without sacrificing intellectual rigor. Ideal for polishing drafts after the core argument and structure are finalized.

This guide teaches how to write a mandatory Significance Statement for scientific reports (like PNAS Nexus). Unlike an abstract, this statement must explain, in plain language, why the research matters to science and society—the 'so what?'. It adheres to strict length guidelines (50-120 words) and is written for a broad, non-specialist audience.

This guide provides strict guidelines for authors writing the "Related Work" section for top-tier programming language theory conferences like POPL. It emphasizes moving beyond general literature reviews to pinpoint precise, technical "delta sentences" that prove novelty against neighboring formal systems. It covers citation standards, venue mapping, and attribution verification for maximum academic impact.

This guide helps researchers determine the optimal venue (like POPL, PLDI, OOPSLA) for their academic papers on programming languages. It advises authors on whether their core contribution is theoretical (proofs, semantics), implementation-focused (compilers, benchmarks), or empirical (user studies), ensuring their work reaches the most appropriate specialized reviewer community.

Covers best practices for writing highly technical academic papers, particularly in programming language theory. It guides authors on structuring their work to build a strong 'ramp' from initial concrete programs and informal motivation to rigorous formal proofs and theorems, ensuring clarity and avoiding common pitfalls like unmotivated formalism or ambiguous notation.

A comprehensive guide and style checker for revising applied economics manuscripts (e.g., for REStat). It ensures the prose is 'empirical-first,' immediately presenting the applied question, the headline estimate (with units and SE), and the design. It mandates a concise abstract (≤100 words) that clearly states the finding with a number, moving the writing away from vague, passive academic language toward plain, quantitative prose.

A comprehensive guide tailored for authors submitting manuscripts to *Scientia Sinica Informationis* (SSI). It details the mandatory high-impact structural arc (Problem, Gap, Contribution, Evidence, Significance), covers dual-language abstract requirements, and specifies rigorous formatting rules for units, variables, and citations, ensuring adherence to top-tier academic standards.

SymPy is a powerful Python library designed for symbolic mathematics. Unlike numerical solvers, it performs exact calculations using mathematical symbols, allowing users to model and solve complex problems across various domains. Core capabilities include symbolic calculus (derivatives, integrals, limits), advanced algebra, solving systems of equations, linear algebra (determinants, eigenvalues), and specialized modules for physics and number theory. It can also generate executable code in languages like C and Fortran, making it invaluable for academic research and scientific computing.

Build a local multimedia webpage project from a user topic, audience, language, style, and media preferences by framing requirements, researching, planning, acquiring media, generating files, packaging, validating, repairing, and delivering usage guidance.

This skill helps developers programmatically discover, filter, and inspect the entire OpenMed model registry. It allows users to find the correct model key for specific clinical tasks (e.g., diseases, oncology, PII detection), filter by language, size, or task, and review model metadata before loading. It pairs with `loading-openmed-models`.

Automates the entire Continuous Integration (CI) workflow, crucial before opening a Pull Request (PR). It systematically runs local unit tests for multiple languages (Python, Node, Rust), performs type checking, pushes the current branch to the remote repository, and finally provides the direct URL and status check for the CI platform (GitHub Actions/GitLab). This single command replaces multiple manual steps.