Skill UI

This workflow suite provides robust tools for automating complex, multi-step software development tasks. It allows users to define reusable workflows (like feature scaffolding, deployment pipelines, and quality checks) that are triggered via slash commands. By integrating persistent 'Memories', it maintains project context across sessions, significantly reducing repetitive prompting and standardizing development practices.

Guides the setup of a high-efficiency, AI-augmented local development loop. This skill integrates features like Cascade editing, in-IDE Previews, and Turbo mode command execution, enabling developers to quickly iterate, debug, and commit changes within a structured environment. Ideal for setting up new projects or optimizing existing development workflows.

This guide outlines the Windsurf reference architecture, providing an optimal project structure for development, configuration, and AI context management. It covers rules hierarchy, workflow organization, monorepo strategies, and context pinning to maximize the effectiveness of AI assistants and development tools.

This guide outlines critical reliability patterns for integrating advanced AI coding assistants into professional development workflows. It emphasizes best practices such as mandatory Git checkpointing, utilizing dedicated feature branches, breaking large tasks into incremental steps, and implementing comprehensive validation gates (linting, testing). Use this when developing fault-tolerant, AI-assisted applications to prevent accidental code breaks and maintain project stability.

This tool provides a comprehensive workflow for generating powerful, self-contained frontend artifacts for AI platforms. It initializes a React/TypeScript project using Vite and Tailwind CSS, allowing developers to build complex UIs. The core function is bundling the entire application into a single, shareable HTML file, making it easily viewable within AI conversations.

Manage microscopy data with the OMERO Python API: access projects/datasets/images, retrieve and analyze pixel data, handle ROIs and annotations, store results in tables, and automate batch workflows for high-content screening.

A Python materials science library for constructing, analyzing, and converting crystal structures, computing phase diagrams and electronic properties, and integrating with the Materials Project API for high-throughput workflows.

Apify Actorization is a process that transforms existing, standalone software or scripts (like CLI tools) into standardized, reusable serverless applications called Actors. Actors are packaged as Docker images, accepting structured JSON inputs and producing structured JSON outputs. This skill guides developers on migrating projects to the Apify platform, ensuring compatibility, and enabling scalable, event-driven data processing pipelines.

Generates practical and robust CI/CD pipelines by programmatically detecting the project's entire technology stack (language, runtime, tooling). It provides fast baseline setup for GitHub Actions or GitLab CI, ensuring repeatability, environment awareness, and alignment with project dependencies. Ideal for onboarding new repositories, standardizing DevOps workflows, or migrating existing deployment processes.

GeoMaster is a comprehensive geospatial science skill that guides remote sensing, GIS, spatial analysis, and machine learning workflows for Earth observation and planetary data projects, covering satellite imagery, vector/raster math, point clouds, terrain, hydrology, and cloud-native data stacks.

A command-line utility designed to search, extract, and analyze data from Burp Suite project files (.burp). It allows users to search request/response headers and bodies using complex regex patterns, extract security audit findings, or gather proxy/site map data. Essential for deep analysis of captured HTTP traffic in web security testing, while providing safeguards against large data overflows.

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.