Skill UI

This elite workflow simulates the comprehensive process of a Senior Software Engineer and Product Designer. It enforces a rigorous development pipeline: starting with planning, solid architecture, and TDD implementation, through clean code practices, and technical auditing. Crucially, it includes mandatory, pixel-perfect UX visual validation to ensure the output is flawless, consistent, and production-grade.

This advanced workflow emulates an elite Senior Software Engineer and Senior Product Designer, ensuring maximum quality output. It enforces a rigorous, multi-step process including ultra-fast planning, solid architecture design, TDD implementation, clean code refactoring, technical auditing, and crucially, mandatory pixel-perfect UI/UX validation. Use this for complex projects demanding both robust code and flawless visual presentation.

This skill demonstrates the multi-model pattern for creating robust and clean API contracts using Pydantic. It guides the creation of separate models for creation requests (Create), updates (Update), responses (Response), and database documents (InDB), ensuring type safety and clear data validation for Python backends. It also covers best practices like handling camelCase aliases and optional fields.

This guide details how to integrate Vast.ai's GPU provisioning capabilities into automated CI/CD pipelines, such as GitHub Actions. It enables developers to run GPU-accelerated tests, model benchmarks, and validations automatically. Key features include robust instance lifecycle management (provisioning, running, and cleanup) and advanced cost-control mechanisms (e.g., setting budget limits) to ensure reliable and efficient testing in a cloud environment.

This tool manages Git worktrees for parallel development on a single repository. It creates isolated worktrees with deterministic naming, auto-allocates non-conflicting ports, and syncs environment files. It supports multi-agent workflows by ensuring each agent has its own branch and port. Additionally, it detects and cleans up stale worktrees safely. Ideal for feature development, hotfixes, and PR validation without branch conflicts.

A comprehensive guide detailing security best practices for integrating and managing enterprise search with Glean. It covers critical topics such as indexing token management, validating webhook signatures, implementing input validation using Zod, and techniques for data redaction, ensuring that sensitive information is protected across the entire search lifecycle.

A comprehensive development workflow solution for integrating with the Glean enterprise search API. It enables developers to simulate local search responses, mock data sources, and test custom connectors and UIs without needing a live Glean deployment. Users can toggle between mock mode for rapid iteration and sandbox mode for validation against a real Glean instance, ensuring a fast feedback loop for building robust enterprise search applications.

This comprehensive guide details the setup and deployment workflow for Glean enterprise search across development, staging, and production environments. It emphasizes strict environment isolation by managing separate datasources, API tokens, and configurations for each stage. The skill covers best practices for promotion workflows, validation steps, and error handling, ensuring search relevance and data integrity during scaling and updates.

This advanced code reviewer analyzes your codebase through the principles of 12 classic software engineering books (e.g., Clean Code, DDIA, Mythical Man-Month). It detects conceptual flaws, design smells, tight coupling, missing abstractions, and architectural risks—issues that standard linters and basic AI tools often miss. Essential for major refactoring and pre-design validation.

This guide details best practices for submitting research artifacts to academic conferences, following the ACM Artifact Review and Badging scheme. It covers everything from required packaging plans (Docker, README, data provenance) to achieving various levels of validation (Functional, Reusable, Reproduced). Learn how to structure your submission to ensure maximum reproducibility and pass rigorous evaluator checks on a clean machine.

Automates the creation of isolated Git worktrees for safe and efficient feature development. This process handles branch validation, optimizes dependency installation (e.g., symlinking node_modules), runs background verification tests, and provides guidance for database isolation, ensuring developers always work in a clean, separated environment.

This skill automates the identification and removal of unused AWS resources such as EBS volumes, snapshots, and Elastic IPs to reduce cloud costs. It includes scripts for dry-run validation, cost impact calculation, and Lambda-based automation workflows. Users can safely clean up storage, compute, and networking waste while ensuring dependency checks and rollback plans are in place.