# Forming An Openrig Mental Model

Use when the system around you does not make sense yet: you just booted into a seat and...

- Repository: https://github.com/mvschwarz/openrig (8541 stars)
- Category: Engineering
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add mvschwarz/openrig --skill forming-an-openrig-mental-model -g -a claude-code -y`
Codex: `npx skills add mvschwarz/openrig --skill forming-an-openrig-mental-model -g -a codex -y`
Cursor: `npx skills add mvschwarz/openrig --skill forming-an-openrig-mental-model -g -a cursor -y`
Gemini CLI: `npx skills add mvschwarz/openrig --skill forming-an-openrig-mental-model -g -a gemini-cli -y`

## Description

Use when the system around you does not make sense yet: you just booted into a seat and do not know how the pieces fit; someone said rig, pod, seat, fleet, topology, or slice and you are not certain what they mean here; you are unsure what kind of rig you are in or what it is for; you do not know how skills reach you or where context comes from; or you are about to act on a guess about how OpenRig works. Gives the runtime mental model fast, so you stop guessing.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Forming an OpenRig Mental Model

You're new to OpenRig, or returning after time away, and you need to
quickly understand what kind of system this is, what your seat is, and what
the moves are. This skill is the fast on-ramp.

For depth, read the canonical reference docs the skill points to. This
skill's job is to get you *oriented*, accurate enough to operate, fast
enough to be useful, not to replace the canonical docs.

---

## The 60-second mental model

OpenRig is a **local control plane for multi-agent coding topologies**. You
declare a topology of agents in YAML, boot it with one command, and OpenRig
manages tmux sessions, harness lifecycles, transcripts, snapshots, and
restoration. When the system goes down, OpenRig snapshots; when it comes
back, agents resume their conversations.

The product loop:

```
down (auto-snapshot) → up <rig-name> (auto-restore) → work → repeat
```

The unit of work is the **rig**, a topology of agents working together as
a single system.

---

## The four-layer model (where you live)

Everything in agent engineering happens at one of four layers. **OpenRig
operates at Layer 3.**

| Layer | Name | Analogy | What it is |
|---|---|---|---|
| L0 | Model | CPU | Foundation model, Claude, GPT, Gemini. Stateless tokens-in/tokens-out. |
| L1 | Agent Core | Process loop | The reason-and-act cycle: observe, plan, choose, act, repeat. |
| L2 | Harness | Container / OS | Tools, memory, lifecycle around the model. Examples: Claude Code, Codex CLI. |
| L3 | Rig | Docker Compose / Terraform | Multi-agent topology, what agents exist, how they relate. **OpenRig.** |

You are an agent at L1 inside an L2 harness, configured by L3 OpenRig.
OpenRig manages your harness; the harness wraps the model; the model
generates your tokens.

---

## Three pillars of context

OpenRig is built on three context-engineering pillars. When you're oriented,
you should know which pillar you're operating in:

The rest: https://github.com/mvschwarz/openrig/blob/HEAD/packages/daemon/assets/plugins/openrig-core/skills/forming-an-openrig-mental-model/SKILL.md