More than a skill manager — manage skills, MCP servers, plugins, hooks, CLIs, configs, memory & rules across every AI coding agent. 🌟 Star if you like it!

Real-time visualization of Claude Code agent orchestration — see your agents think, branch, and coordinate as they work.

See how you really use AI — X-ray your AI coding sessions locally

Real-time AI coding agent status panel in your MacBook notch — live status, approvals & replies for 13 AI tools, with iPhone & Apple Watch companions

Real-time architectural sensor that helps AI agents close the feedback loop, enabling recursive self-improvement of code quality. Pure Rust.

🚀 A real-time monitoring dashboard for Claude Code & Codex, built with SQLite3, Node.js, Express, React, Vite, TailwindCSS, & WebSockets. It tracks sessions, agent activity, tool usage, and subagent orchestration, providing live analytics, a Kanban status board, status notifications, a cute buddy, & an interactive web UI/MacOS/Windows native app.

A desktop pet for macOS & Windows that monitors your AI coding agents (Claude Code, Codex, Cursor, Gemini...) in real time, and grows as you code, feed it tokens, level it up, climb the leaderboard.

Cut context bloat in your AI-agent stack: find and safely prune unused skills, MCP servers and subagents from real transcript evidence

One command for every interface—search, run, and inspect real software across APIs, browsers, desktops, local tools, and MCP.

Run any process, on your machine or in an AI agent's environment, as if it were a pod in your Kubernetes cluster: real env vars, DNS, network, traffic.

Free, open-source SQL Server and Postgres performance monitoring. Collectors, real-time alerts, graphical plan viewer, MCP server for AI analysis. Supports SQL 2016-2025, Azure SQL, AWS RDS, Postgres, Aurora Postgres.

Research-first architecture engine for Python, TS, Go and Rust. Mines GitHub Issues for real production failures before scaffolding, then audits drift, cycles and fragility in code you already have. Zero import cycles, zero critical anti-patterns - measured against itself.

The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.

mission control for agent skills