Skill UI

This skill guides users through Phases 2 (Pseudocode Design) and 3 (Architecture Design) of the SPARC methodology. It translates high-level specifications into detailed technical blueprints, covering algorithm pseudocode, module boundaries, API contracts (using DDD patterns), and infrastructure planning, ensuring a robust plan before writing production code.

This workflow guides developers through Test-Driven Development (TDD) principles. It mandates writing tests (unit, integration, E2E) before writing implementation code, ensuring minimum 80% test coverage. The structured process (Red -> Green -> Refactor) guarantees robust code quality, effective bug fixing, and safe feature addition, while integrating Git checkpoint commits for full traceability.

This skill guides users through evidence-based terminal operations. It is designed for scenarios requiring direct command execution, Git status checks, debugging CI/build failures, and pushing narrow, verified fixes. It emphasizes following a structured, minimal-scope workflow and accurately reporting the repository's current state (e.g., local changes vs. pushed code).

This tool automatically converts design screenshots or visual exports into structured, functional Vue 3 Composition API components. It supports popular UI libraries like Vant, Element Plus, and Ant Design Vue. By batch converting screenshots, it streamlines the development process from raw design assets to reusable, production-ready code, significantly boosting frontend development efficiency.

This guide provides a detailed strategy for managing and releasing research artifacts (code, models, data) associated with a vision paper submitted to ECCV. It covers two critical phases: the sealed, anonymous archival submission during review, and the structured public release runway post-acceptance, ensuring reproducibility and adherence to conference policies.

This comprehensive guide outlines the strict requirements for preparing replication and data packages for submission to The Econometrics Journal (EctJ). It covers everything from README structure and detailed code inclusion to ensuring Monte Carlo determinism and managing restricted data, ensuring the method's credibility for reviewers.

Provides crucial guidelines for structuring research papers submitted to ICDM. It details how the 10-page IEEE limit must accommodate the body, in-cap appendices, and references, while effectively offloading bulk material (code, full logs) to an external repository. Essential for avoiding rejection due to page overruns.

This guide provides comprehensive best practices for authors submitting software artifacts to academic venues like ICSME. It details how to structure packages, track provenance, and document code to achieve the IEEE Open Research Object and Research Object Reviewed badges. Learn how to design for rigorous, reproducible evaluation by evaluators in a bounded time frame.

This guide provides comprehensive instructions for structuring and refining manuscripts intended for IMFER, ensuring the prose resonates with both academic referees and policy readers. It covers the structure of the introduction, the format of the abstract (including JEL codes and italicization), and maintains consistency in style, tone, and quantification.

This guide outlines best practices for packaging complex research artifacts—including simulators, codebases, and datasets—to maximize credibility and reproducibility. It is essential for authors publishing in venues like INFOCOM where formal artifact evaluation tracks are not mandatory. Learn how to structure your submission to build reviewer trust and ensure your findings can be independently verified.

A comprehensive guide for researchers on how to structure, package, and document complex scientific artifacts (e.g., simulation results, specialized code) to ensure reproducibility for academic reviewers. It details setting scope tiers (Reproduce, Regenerate, Inspect) and mitigating technical challenges like build fragility and runtime discrepancies.

Systematically transforms high-level ideas or goals into a detailed, structured, and executable taskbook designed for AI agents. This process involves deep research, validation against existing codebases, adherence to strict formatting rules, and generating a single, copy-pastable block of instructions for immediate agent execution. It distinguishes between execution and exploration tasks, ensuring all steps are verifiable and anti-cheating measures are included.