A management layer for AI agent skills — discover, install, scope, rate, and update skills across Cursor, Codex, and Claude Code.

Skill Mix

A management layer for AI agent skills — discover, install, scope, rate, and update skills across Cursor, Codex, and Claude Code.

All three tools follow the open Agent Skills standard (SKILL.md), but none solves the management problem: how to find the right skill, avoid duplicates, track where it came from, or know if it's any good.

Setup

Quick install Skill Mix, open the app, and launch the skill-picker modal:

npx -y skill-mix obra/superpowers

Classic installer:

curl -fsSL https://raw.githubusercontent.com/razbakov/skills-manager/main/scripts/install.sh | bash

Usage

Run skills to launch application

Open/focus the app and trigger the skill-picker from CLI:

Build DMG (macOS)

bun install
bun run dist:dmg

This outputs a DMG installer in release/.

Problem

See Research for how tools handle skills today, User Problems for 14 documented pain points, Landscape for existing directories and tools, and Competitors for detailed analysis of current solutions and their gaps.

• Naming collisions — Multiple skills share similar names or overlap in responsibility. Cursor has no detection; Codex shows both without merging; Claude Code overrides by scope precedence.
• No development workflow — No way to test, enable, or disable skills without editing files by hand (Codex has config.toml, but no UI).
• Unknown provenance — None of the three tools track where a skill came from, who wrote it, or how to update it.
• Scattered collections — Skills live in 3–6 different directories per tool with no unified view.
• No quality signals — No ratings, benchmarks, or reviews exist anywhere in the ecosystem.

Key Features

• Skill Registry: Search, browse, and install skills from a shared catalog wit