Gabo — AI skill for Claude Code
GABO is a web-based multi-agent harness for AI Platforms.
How to install Gabo
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open Gabi-comm/Gabo and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What Gabo does
GABO is a web-based multi-agent harness for AI Platforms. It primarily used for multi-modal AI such as Claude Code, OpenAI and Gemini. It sit twelve agents with sharp, opinionated roles, and rooms that pull the right ones for a kind of work.
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README
Gabo
A multi-agent harness that runs on your own machine.
A team of twelve role agents works together in rooms built for studying, brainstorming and shipping software.
Gabo is a local web app built on the [Claude Agent SDK](https://code.claude.com/docs/en/agent-sdk/overview). It works like a terminal coding agent (tools, permissions, slash commands, history, plugins, skills, MCP servers), and adds a team of role agents who hand work to each other, challenge each other and build on each other's output.
It runs on the AI account you connect: a **Claude**, **OpenAI** or **Gemini** API key, or a free **local model** in Ollama.
Features
**Twelve agents with distinct roles:** Believer, Skeptic, Investor, Judge, Designer, Coder, Tester, Researcher, Tutor, Caveman, Planner and Emperor. Each has a pixel mascot and its own stage.
**Rooms where the team works together:**
- Library (study and research)
- Arena (brainstorm and pick the best idea, with an idea tournament)
- Hackathon (plan, design, build, test, ship)
- Laboratory (you pick the team)
In every room, agents answer each other's points and pass work along, and the chat shows who builds on whom.
**Team size that fits the prompt:** a router picks a tier with no model call. It scores the prompt: things you ask it to produce (a plan, questions), amounts ("3 examples"), several steps ("then…"), lookups and length. Plain short questions are Quick.
- Quick: short questions. The team answers together in one reply.
- Standard: regular tasks. Like Quick, with at most one real agent when a role needs tools.
- Deep: build work, long tasks, the Arena tournament. Real agents, with independent ones running in parallel.
A team-in-one-reply answer used about half the tokens of a real-agent run
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