Skill UI

A comprehensive three-column layout template designed for high-readability technical documentation, such as API references, tutorials, and guides. It features a sticky side navigation, main article body with support for code blocks and callouts, and a scroll-spy Table of Contents (TOC). Includes search and version control.

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 comprehensive guide outlines industry best practices for designing robust and consistent REST APIs. It covers proper resource naming conventions, standardized HTTP method semantics, detailed HTTP status codes (e.g., 201, 404, 422), structured response formats (including pagination and links), and advanced strategies like cursor-based pagination. Essential for building scalable and maintainable APIs.

Automatically generates comprehensive API documentation from existing TypeScript or JavaScript source code. It scans public exports, parses detailed JSDoc annotations (including parameters, returns, and throws), and can generate standardized OpenAPI 3.0 specifications for HTTP endpoints, ensuring robust and up-to-date API references.

A comprehensive guide covering scalable backend architecture patterns, including Repository, Service, and Middleware design. It details best practices for building robust APIs using Node.js, Express, and Next.js. Topics covered include RESTful API structuring, database query optimization (preventing N+1 issues), implementing database transactions, and integrating caching strategies like Redis. This guide is essential for developing high-performance, maintainable server-side applications.

A comprehensive tool for establishing performance baselines across multiple dimensions. It measures Core Web Vitals (LCP, CLS, INP), API latency (p50, p95, p99), and development build efficiency. Essential for detecting performance regressions before merging code, ensuring stability in CI/CD pipelines, and comparing alternative stack implementations.

This skill provides comprehensive web browser automation capabilities for agents. It utilizes AI-optimized snapshots, reducing context size by 93% by using element references instead of full DOM. Functions include navigation, interactive element extraction, form filling, data scraping, and state management across multiple isolated sessions. Ideal for complex web testing and data extraction workflows.

This skill is a compatibility shim for the older browser-scrape function. It is deprecated and advises users to migrate to the more robust `browser-extract` skill. The new skill offers advanced features like cognitive containers, PII detection, and automatic selector management for reliable web scraping.

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.

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.