Skill UI

Expert skill for building modern, production-ready Chrome Extensions using Manifest V3 standards. Covers advanced topics including service worker management, content script injection, secure cross-context communication (message passing), and implementing best practices like `declarativeNetRequest` and secure storage APIs.

Defines a Markdown/Mermaid-first documentation skill for scientific and technical writing, providing diagram standards, 24 reference types, and templates so every report, README, or analysis stays text-first, versionable, and visualization-ready.

Automates iterative use of the plugin-dev skill-reviewer agent to diagnose and fix Claude Code SKILL.md issues, driving repeated review/fix cycles until standards are met before emitting the completion marker.

Generates complete, production-ready landing pages using Next.js/React and Tailwind CSS. It incorporates proven copywriting frameworks (AIDA, PAS, BAB), optimizes for SEO with structured data, and adheres to Core Web Vitals standards (LCP, CLS). Ideal for creating high-performing marketing homepages, lead capture forms, and campaign sites.

This skill is intended to analyze and improve the quality of codebases, ensuring adherence to best practices and coding standards. Note: This specific file is a translation placeholder, and the core functionality description should refer to the original skill file for details.

Guided workflow for turning PM notes or rough drafts into compliant repo skills, covering draft generation, validation checks, README updates, and optional packaging so every submission passes the repo standards.

This skill enforces a rigorous, opinionated baseline for UI development, ensuring consistency across spacing, hierarchy, typography, and layout. It helps maintain high code quality by fixing structural and stylistic inconsistencies, particularly useful for polishing AI-generated or quickly developed interfaces to meet professional standards and accessibility requirements.

This skill enforces Sentry's rigorous blog writing standards, ensuring all content—from deep technical dives to product announcements—reads like it was written by a senior developer. It guides users on adopting a specific, opinionated, and technically credible voice, structuring posts around problem-solving, and avoiding generic corporate jargon.

This skill helps users create a new Git branch that strictly adheres to established project naming conventions (e.g., Sentry standards). It automatically determines the appropriate branch type (such as `feat`, `fix`, `chore`, etc.) and generates a descriptive, standardized branch name by analyzing the provided work description or local code differences (diff). It guides the entire branching workflow, ensuring proper lineage and classification for code submissions.

This skill provides deep technical expertise regarding the DWARF debugging format (v3-v5) and the DWARF standard. It assists in analyzing, parsing, verifying, and generating debug information from compiled binaries. Use cases include answering questions about the DWARF specification, writing code that interacts with DWARF data, and utilizing tools like `dwarfdump` or `readelf` for structural validation and quality metric extraction.

This specialized skill acts as an expert AI auditor for Food Safety Modernization Act (FSMA) compliance. It is designed to review critical records, including HACCP plans, Preventive Controls, and Supply Chain documentation for food processing facilities. Use it to simulate FDA inspections, identify compliance gaps (such as missing CCPs or inadequate corrective actions), and receive actionable guidance to ensure the highest standards of food safety.

This skill provides comprehensive tools for documenting and managing model performance on Hugging Face. It supports multiple methods for adding structured evaluation data, including extracting tables from README content, importing benchmark scores via APIs (like Artificial Analysis), and executing high-performance custom evaluations using advanced backends such as vLLM or lighteval. It ensures that all metadata is structured and compatible with model leaderboards and industry standards.