Skill UI

This file serves as the localization resource for the Liquid Glass Design skill, specifically targeting Japanese translation. Please note that this file is currently under translation development. For the complete functionality and core documentation, refer to the original skill file.

This skill manages the secure transition of private internal projects to public open-source releases. It executes a three-stage pipeline: Forking (removing secrets), Sanitizing (verifying clean code), and Packaging (generating required documentation like README, setup.sh, and CLAUDE.md). It ensures code safety and readiness before public deployment.

This skill executes the final two phases of the SPARC methodology: Refinement and Completion. It systematically reviews the implemented feature through rigorous code reviews, expands test coverage (unit, integration, edge cases), validates performance, generates a traceability matrix, creates comprehensive documentation, and confirms deployment readiness. It ensures the feature is robust, tested, and production-ready.

This skill acts as an expert guide for creating comprehensive, implementation-ready design system documentation. It structures guidelines by defining foundational tokens (color, typography, spacing), detailing component anatomy, interaction states, and adhering to strict accessibility standards (WCAG 2.2 AA). Use it to ensure your design standards are consistent, technically sound, and actionable for engineering and design teams.

This powerful tool utilizes Playwright to record high-quality, professional web application demo videos. It simulates natural user interactions, injecting a visible cursor overlay and adhering to specific pacing guidelines. It is ideal for creating comprehensive tutorials, feature walk-throughs, or documentation for stakeholders.

A comprehensive final preflight guide for submitting manuscripts to the American Journal of Political Science (AJPS) via Editorial Manager. It covers strict requirements for double-blind review, including anonymization rules, word count limits, formatting (APSA/Chicago), Supporting Information caps, and human subjects documentation, ensuring the manuscript is ready before hitting submit.

This skill offers two complementary workflows for locally checked-out codebases. Documentation mode creates a definitive, cited architecture document ideal for onboarding and system research. Modernization mode then builds upon this documentation, generating a phased, safety-laddered plan to upgrade, migrate, or rewrite legacy systems. It emphasizes local-first analysis for maximum accuracy.

A comprehensive guide for preparing research artifacts (code, data, prompts, models) for EACL submissions. It details the two stages: an anonymized supplement for the initial review (ARR) and a fully licensed, public release for post-acceptance. Emphasis is placed on reproducibility, responsible NLP practices, and proper documentation.

This skill generates validated local artifacts (PNG, JPEG, or PDF) from public HTTP(S) web URLs using the Latchshot service. It is ideal for creating visual reports, quality assurance documentation, or social media previews of public web content. Note that this tool only supports public pages and cannot access private, authenticated, or restricted content.

This guide provides authors with comprehensive best practices for submitting research artifacts (e.g., compilers, benchmarks, proofs) for post-acceptance evaluation at PLDI. It details how to structure the submission, ensuring reproducibility, containerization, and clear documentation to achieve Functional, Reusable, or Available badges.

This comprehensive guide outlines the requirements for submitting computational artifacts for post-acceptance evaluation at PPoPP. It details PPoPP's unique badge policy (Functional, Reusable, Results Reproduced), addresses the challenges of ensuring results reproduce across different hardware architectures, and provides a detailed packaging plan, including containerization, README structure, and provenance documentation for maximizing reproducibility.

This guide details best practices for achieving high standards of reproducibility in visual analytics papers, focusing on open materials, verifiable code, and rigorous documentation. It covers mapping figures to data sources, preregistering studies, documenting data provenance, and defining the level of reproducibility achieved (e.g., turnkey, scripted).