lazyclaude: A TUI for Managing Claude Code Configuration
lazyclaude is a terminal user interface (TUI) tool built in Rust with ratatui that provides centralized management for Claude Code configuration files stored on disk. It's inspired by lazygit and offers a single interface for working with various configuration types.
Key Features
The tool allows you to:
- Browse and edit important config types across user and project scopes
- Create, delete, and search skills, agents, and MCP servers
- Search and install from the skills registry (anthropics/skills) and npm MCP packages
- Use fuzzy filtering, clipboard support, and JSON export for scripting
- View settings diff showing which scope defines each setting
Installation
Install via Cargo:
cargo install lazyclaudeOr download prebuilt binaries from GitHub Releases.
The tool manages these Claude Code configuration elements: memory files, skills, agents, MCP servers, settings, permissions, hooks, keybindings, sessions, stats, plugins, and todos.
lazyclaude is MIT licensed and available on GitHub at https://github.com/idossha/lazyclaude. The developer welcomes feedback and contributions.
📖 Read the full source: r/ClaudeAI
👀 See Also
Simplifying Automation with OpenClaw Wrappers
OpenClaw Wrappers offer an efficient way to manage AI coding agents. Discover how these tools integrate easily into existing frameworks with specific command examples and community feedback.
Open-source MCP memory server with knowledge graph and learning features
An open-source MCP server written in Rust provides persistent memory for AI agents with knowledge graph architecture, Hebbian learning, and hybrid search. It's 7.6MB with sub-millisecond latency and works with any MCP-compatible client.
Building a Programming Language with Claude Code: The Cutlet Experiment
Ankur Sethi built a complete programming language called Cutlet using Claude Code over four weeks, with the AI generating every line of code while he focused on guardrails and testing. The language features dynamic typing, vectorized operations, and a REPL, running on macOS and Linux.
GLiGuard: Open-Source 300M Parameter Safety Moderation Model Claims 16x Speedup Over LLM Guardrails
Fastino Labs releases GLiGuard, a 300M parameter encoder-based model that performs multiple safety tasks in a single pass, matching or exceeding models 23–90x larger while running up to 16x faster.