Hey community!
I want to share an open-source tool I built for myself, and that I hope will be useful to other developers working with AI agents.
The Problem
The more AI coding tools we adopted, the messier our setup got. Skills and MCP servers installed manually, via individual commands, or copy-pasted from docs. Scattered across Claude Code, Cursor, Codex, Windsurf - with no way to version any of it, no way to share it with teammates, no way to reproduce it on a new machine or project.
Every new team member meant walking them through the same manual steps. Every new machine meant doing it all over again. And if someone changed a skill or added an MCP server, there was no way to propagate that to the rest of the team.
Sound familiar?
The Idea
I built kasetto to fix that. The idea is borrowed from things I already loved - the declarative reproducibility of dotfiles, the simplicity of uv for Python packages. What uv did for Python, kasetto aims to do for AI agent skills.
One YAML config describes your entire setup: skills, MCP servers, target agents. Commit it, share it, and everyone on the team gets the exact same environment. No manual editing, no drift between machines or teammates. New machine? One command.
The Name
Kasetto (カセット) is the Japanese word for cassette - as in a cassette tape. A cassette was a self-contained, portable thing: you recorded something once, handed it to someone, and it played back exactly the same on any device. No setup, no drift, no "works on my machine." That's exactly what kasetto does for your AI agent environment - package it once, share it, and it reproduces identically everywhere.
Why kasetto
- Declarative - one YAML config, version it, share it, bootstrap a whole team in seconds
- Multi-agent - 21 built-in presets: Claude Code, Cursor, Codex, Windsurf, Copilot, Gemini CLI, and more
- Multi-source - pulls from GitHub, GitLab, Bitbucket, Codeberg including self-hosted and enterprise
- MCP management - merges MCP servers into each agent's native settings file automatically
- Global and project scopes - install skills globally or per-project, each with its own lockfile
- CI-friendly -
--dry-run to preview, --json for automation, non-zero exit on failure
- Single binary - no runtime dependencies, written in Rust, fast cold starts
How It Works
Define your config:
agent:
- claude-code
- cursor
- codex
skills:
- source: https://github.com/org/skill-pack
skills: "*"
- source: https://github.com/org/skill-pack-2
skills:
- product-design
- source: https://github.com/org/skill-pack-3
skills:
- name: jupyter-notebook
path: skills/.curated
mcps:
- source: https://github.com/org/mcp-pack
Then sync:
kst sync
That's it. Kasetto pulls the skills and MCP servers, installs them into the right agent directories, and merges MCP configs into each agent's native settings file. The next time you run sync, only what changed gets updated.
Command Overview
Kasetto ships with a focused set of commands - each does exactly one thing:
kst sync # pull skills + MCP servers and install them
kst sync --dry-run # preview what would change without touching anything
kst sync --json # structured output for CI pipelines
kst list # browse installed skills and MCPs interactively
kst list --json # dump installed state as JSON
kst init # scaffold a kasetto.yaml in the current directory
kst doctor # check your environment and diagnose issues
kst clean # remove all installed skills and MCP entries
kst self update # update kasetto to the latest release
kst self uninstall # remove kasetto and all its files
kst completions # generate shell completions (bash, zsh, fish, etc.)
The binary is available as both kasetto and kst - the short alias makes daily use quick.
Install
macOS / Linux
curl -fsSL https://raw.githubusercontent.com/pivoshenko/kasetto/main/scripts/install.sh | sh
Homebrew
brew install pivoshenko/tap/kasetto
Cargo
cargo install kasetto
What's Next
The roadmap includes agents management and hooks management. If you have ideas or run into issues, open an issue - feedback from real users shapes the direction.
github.com/pivoshenko/kasetto
If kasetto saves you time, a GitHub star means a lot. And drop a comment below - I'd love to hear what your AI agent setup looks like today, and what pain points you're still hitting.
