Skill UI

Neon Object Storage provides S3-compatible, branch-aware object storage directly integrated with your Neon Postgres project. It ensures that files (images, documents, user uploads) remain perfectly synchronized with the database schema across all development branches (dev, preview, staging, production). Use this skill when you need a single backend for both database rows and file assets, eliminating the need for separate cloud storage providers.

This skill generates comprehensive, production-grade unit and integration tests using the Python pytest framework. It covers advanced patterns such as fixtures (with scope and teardown), parametrization, custom markers, mocking external dependencies, and best practices for writing robust and maintainable test suites. Use this when implementing detailed test coverage for Python code.

Provides championship-grade repository hygiene and health checks. It systematically audits critical areas, including dependency health, CI/CD pipeline alignment, documentation synchronization, and detecting stray artifacts. The tool generates a prioritized, actionable cleanup plan and offers auto-fix capabilities to keep codebases pristine and production-ready.

A comprehensive skill for generating production-grade, robust automation scripts and end-to-end tests using Selenium WebDriver. Supports multiple languages (Java, Python, JS, C#, etc.) and execution environments, including local machines and TestMu AI cloud for cross-browser compatibility. It guides users on best practices like explicit waits, Page Object Model (POM), and stable locator strategies, acting as a senior QA automation architect.

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.

This skill monitors for regressions on deployed URLs following deployments, merges, or dependency upgrades. It performs comprehensive checks including HTTP status, console errors, network failures, performance metrics (LCP/CLS/INP), and crucial API endpoint health, ensuring service stability in production or staging environments. Supports quick checks, continuous monitoring, and differential comparison against baselines.

A comprehensive pattern for building robust and cost-efficient LLM application pipelines. This guide details combining model routing based on task complexity, immutable cost tracking, limited retry logic, and prompt caching to manage API expenditures while maintaining high quality. Essential for production systems handling variable-complexity, high-volume tasks.

A comprehensive guide to safe and reversible database migration patterns. Covers zero-downtime schema changes, data backfilling, and rollbacks for major databases like PostgreSQL and MySQL, and popular ORMs (Prisma, Drizzle, Django). Learn techniques like concurrent indexing and the expand-contract pattern to ensure production stability.

This guide outlines the architecture and implementation steps for building a robust, production-ready design system. It covers core elements like design tokens, reusable components, and layout patterns. Ideal for teams needing to maintain design consistency, ensure accessibility, and synchronize design (Figma) with implementation (Storybook).

This guide outlines best practices for building scalable and maintainable Django applications and APIs. It covers recommended project layouts, implementing separated configuration files (dev/prod), advanced ORM techniques, and integrating middlewares, caching, and signals for enterprise-level robustness. Ideal for professional backend development.