NeoCodeium is a Neovim plugin that provides AI-powered code completion using the Windsurf (formerly Codeium) service. It features a modular architecture with separate modules for state management, completion logic, rendering, server communication, and utility functions. The plugin integrates with Neovim's autocommands to provide real-time suggestions as virtual text, supports cycling through completions, accepting partial suggestions, and includes a chat interface via the Windsurf service. It is designed to be highly configurable and works alongside other completion plugins like nvim-cmp and blink.cmp.
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 is a comprehensive, tool-agnostic skills and steering pack that teaches AI coding agents to perform AWS Well-Architected Framework reviews. It contains a curated reference corpus of over 2,000 markdown files (6 pillars, 307 best practices, 27 industry lenses) derived from official AWS documentation, plus per-tool adapters for 15 AI coding assistants (Claude Code, Cursor, Codex, GitHub Copilot, etc.). The repo also ships production-grade supporting tooling: a CI gating tool (wa-ci) that diffs structured review JSON against a baseline and blocks PRs on regressed gaps; an evaluation harness with blind multi-reviewer adversarial adjudication; a crawler to regenerate the reference corpus from AWS docs; and one-command installers. The core skill dispatches six parallel subagents (one per pillar) to keep the 2.2 MB corpus out of any single context window. Despite the extensive content, the repository has notable maintenance challenges: the reference snapshot will drift as AWS documentation evolves (users are responsible for regeneration); lens files are duplicated across `powers/` and `skills/` directories creating a sync hazard; and the flagship skill is explicitly deprecated in favor of a successor in Agent Toolkit for AWS. The evaluation harnesses and CI tooling are well-crafted and reusable, but the sheer scale of markdown files and platform-specific adapters makes ongoing synchronization complex.
MCPM (Model Context Protocol Manager) is a production-grade CLI tool for managing Model Context Protocol (MCP) servers. It provides a centralized server registry, global configuration, virtual profile system (tags), multi-client integration (Claude Desktop, Cursor, Windsurf, VSCode, Cline, Goose, and 10+ more), direct server execution via stdio/HTTP/SSE, secure public sharing via tunnels, and usage monitoring/analytics. The tool is designed for both interactive use and AI-agent automation (non-interactive mode with environment variables). The repository also contains a curated registry of 400+ MCP server definitions (JSON manifests) with metadata and installation instructions. While primarily a Python CLI application, the registry component adds a hybrid data/configuration dimension. The architecture follows a monolith command pattern with modular command implementations, client adapters using base class inheritance, and optional FastMCP proxy for server aggregation.
This is a collection of 12 lessons on effective AI-assisted software development, derived from real-world experience with tools like Cursor, Windsurf, Cline, and Claude. It is not a software application or data research project—it is pure documentation. The lessons cover project structure, planning, problem decomposition, test writing, commit discipline, and prompt engineering. The content is clear, concise, and immediately applicable. No code or data pipelines are present; the value lies in the reusable practices and mental models it teaches.
This repository is a curated collection of system prompts, tool definitions, and behavioral configurations extracted from dozens of commercial and open-source AI coding assistants and products. It contains over 100 text files organized by vendor (Anthropic, Cursor, VSCode Agent, Manus, Devin, Replit, etc.), each representing a snapshot of how these AI agents are instructed to behave, what tools they can use, and what safety rules they follow. The collection is not an application or a data pipeline; it is a reference archive for researchers, prompt engineers, and developers studying AI agent design patterns, prompt injection risks, and competitive analysis. The repository includes both full system prompts (e.g., Claude Code 2.0, Cursor Agent, Manus) and structured tool JSON schemas that define function-calling APIs. While the content is valuable for understanding AI agent architecture, the repository itself has no executable code, no versioning of individual prompts, and no automated validation, making it a static snapshot rather than a living project.