Gajae-Code

A red-claw coding-agent harness (gjc). Crisp interviews, resilient plans, tmux-native execution, durable verification.

Disclaimer: Gajae-Code is an experimental, beta-stage early project. Expect rough edges and verify outputs before relying on it for important work.

What it is

Gajae-Code is published through the npm registry as gajae-code; that package installs the gjc binary. It keeps the public agent surface intentionally small while making the runtime around it dependable — session state, worktree isolation, tmux orchestration, model routing, tool execution, and persistent evidence are all there when the work needs them.

Rather than a sprawling default skill zoo, the harness improves by making one method better.

The one useful loop

deep-interview -> ralplan -> ultragoal
                         └─ optional team execution when parallel tmux workers help

• Use deep-interview to clarify intent before any planning or code change.
• Use ralplan to build and critique the approach before mutation.
• Use ultragoal to carry the work through implementation, revision, verification, and an evidence summary.
• Add team only when the task benefits from coordinated parallel workers — it is an optional execution mode, not a required handoff step.

The sequence describes the operator flow, not hidden automation: the agent still reports what it changed, what it revised after findings, what checks ran, and what evidence supports the result.

Remember this first

At first, you only need four steps.

1. Prepare Bun, Git, tmux, and provider credentials.
2. Launch with gjc --tmux.
3. Clarify and plan with deep-interview and ralplan.
4. Carry the work with ultragoal; add team only when parallel tmux workers help.

For research instead of mutation, start gjc rlm.

Explore the docs

Next steps

• Getting Started — install, launch, and run the first loop.
• Recommended Environment — check the local setup before installing.
• Source project: Yeachan-Heo/gajae-code
• Community: Discord