Task Master is a monorepo providing an AI-driven task management system designed for integration with AI coding assistants (Claude Code, Cursor, Windsurf, etc.). It consists of a CLI tool (`task-master`), an MCP server, a VS Code extension, and core library packages. The system manages tasks with statuses, priorities, dependencies, tags, and subtasks, and offers AI-assisted operations like PRD parsing, task expansion, complexity analysis, and TDD workflow automation (autopilot). It supports multiple storage backends (local JSON files, remote Hamster API via Supabase) and 15+ AI providers. The architecture follows a clean layered pattern with domain modules in `@tm/core` consumed by CLI and MCP interfaces, though a legacy `scripts/` directory still contains overlapping logic being gradually migrated. The codebase demonstrates strong engineering practices including factory patterns, abstract base classes, dependency injection, atomic file operations, and extensive test coverage. Some technical drift is present due to the ongoing migration from JavaScript to TypeScript and duplicated MCP server implementations.
This repository provides a comprehensive AI-assisted UI/UX design system toolkit, primarily packaged as a skill for AI coding assistants (Claude Code, Cursor, Windsurf, Copilot, etc.) and distributed via the 'ui-ux-pro-max-cli' npm package. Its core functionality includes a curated knowledge base of 79 UI styles, 192 product color palettes, 74 font pairings, 119 UX guidelines, 22 tech stacks, and 192 reasoning rules – all stored as structured CSV files. A BM25 search engine (implemented in Python) enables domain‑aware retrieval, while a design system generator aggregates search results and applies reasoning rules to output complete design system recommendations (colors, typography, style, layout patterns, motion, anti‑patterns). The gallery Next.js app provides a browsable, filterable interface for the style catalog. Architecturally, the project is a hybrid of data curation and application delivery. The canonical source of truth lives in `src/ui-ux-pro-max/`, which is synchronised to `cli/assets/` and `.claude/skills/` via a CI‑enforced sync script. It includes sub‑skills for brand management, slide/presentation generation, banner design, icon/logo generation, and UI styling (with shadcn/ui and Tailwind integration). The CLI tool installs these skills into the appropriate AI assistant configuration directories, with platform‑specific templates for 20+ assistants. Automated workflows refresh Google Fonts and Phosphor icon catalogs weekly, and a suite of Python tests validates data contracts, stack freshness, and search relevance. Despite its active maintenance and rich functionality, the codebase suffers from significant file duplication across three directory trees, hardcoded version pins for frameworks, and test brittleness tied to exact data row counts. The design system generator and search engine are well‑crafted and reusable, but the overall project is tightly coupled to its curated CSV schema and the ecosystem of AI coding assistants. It represents a mature, production‑grade toolkit for developers who want AI‑assisted design generation, but the heavy duplication and external API dependencies (Gemini, Atlas Cloud, MuAPI) introduce moderate technical drift risk.
This is a curated collection repository of Cursor AI editor rules (.mdc files), not a traditional software application. It functions as an awesome-list style repository that aggregates project-specific configuration files for the Cursor AI code editor. The repository contains over 200 rule files covering various programming languages, frameworks, testing methodologies, and development workflows. The architecture is straightforward: a flat collection of .mdc files in the rules/ directory, with a README.md serving as the index and discovery mechanism. The repository includes Node tooling for maintaining quality and consistency: scripts for checking repository hygiene, validating awesome-list format, converting README links between relative and absolute forms, checking PR author age policies, and cleaning up legacy artifacts. The CI/CD pipeline uses GitHub Actions with a main workflow file. The key value proposition is providing reusable Cursor AI configuration files that developers can drop into their .cursor/rules/ directories. Each rule file contains frontmatter metadata (description, globs pattern, alwaysApply flag) followed by instructions that guide Cursor AI's behavior for specific technology stacks or development patterns. The rules range from framework-specific guidance (React, Vue, Next.js, Svelte) to language best practices (TypeScript, Python, Go, Rust) to specialized domains (embedded systems, blockchain, machine learning workflows). The repository demonstrates moderate engineering quality in its maintenance tooling, with centralized configuration, test coverage for scripts, and automated quality checks. However, the rule files themselves vary significantly in quality and depth - some are comprehensive and well-structured while others are minimal or boilerplate.