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Topology Mutation And Seat Management

CommunityPopular
mvschwarz
topology-mutation-and-seat-management

Use when changing a rig while it is alive — `rig grow` / `rig expand` / `rig shrink` / `rig launch` / `rig remove` / `rig discover` / `rig bind` / `rig adopt` / `rig attach`. Covers the 4 failure modes (newly created seat lacks queue/startup/role; edges and permissions not updated; adopt/bind succeeds at tmux but not OpenRig identity; shrink/remove leaves stale topology references) and the rule that mutation must work while the rig is active, not just in clean fixtures.

Overview

Publishermvschwarz
Repositoryopenrig
Skill nametopology-mutation-and-seat-management
Stars
5K
Forks
366
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by mvschwarz on GitHub. Read the source before you install it.

Installation

Install the Topology Mutation And Seat Management AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/topology-mutation-and-seat-management
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mvschwarz/openrig.git /tmp/openrig
mkdir -p .claude/skills
cp -r /tmp/openrig/packages/daemon/specs/agents/shared/skills/core/topology-mutation-and-seat-management .claude/skills/topology-mutation-and-seat-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Topology Mutation And Seat Management in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Topology Mutation And Seat Management on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Topology Mutation And Seat Management is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Topology Mutation and Seat Management

The ability to change a rig while it is alive: expand, shrink, launch, remove, discover, bind, adopt, and attach seats or sessions. Seat management includes the stable identifiers, edges, roles, and startup context that make those mutations coherent.

OpenRig should be easy to reach for. A user should be able to add capacity, retire capacity, adopt an existing session, or attach a terminal without rebuilding the whole topology. If mutation is rarely tested, users avoid it and the product collapses back into static launch scripts.

Use this when

  • Adding seats to a running rig (rig grow <rig-id> <member...> for the simple case, a fragment with rig expand or rig add otherwise; see below)
  • Removing capacity (rig shrink / rig remove)
  • Launching/relaunching a node in a running rig (rig launch)
  • Binding a discovered session into an existing logical node (rig bind)
  • Adopting a topology + binding live sessions (rig adopt)
  • Attaching a shell or agent into a rig node (rig attach --self)

Adding seats: rig grow first, a fragment when you need more

rig grow <rig-id> <member...> adds one or more seats to a running rig without writing YAML. Each seat gets the default agent spec and its default profile. Check rig grow --help on your installed version; at the time of writing:

  • --pod <pod>: the target pod; inferred when the rig has one pod
  • --new-pod <pod>: create a new pod for the seats (not together with --pod)
  • --runtime <runtime>: one runtime for every named seat (default claude-code)
  • --cwd <path>: one working directory for every named seat (default: the current directory)
  • --json: output for agents

Write a fragment instead when a seat needs something rig grow does not set: an explicit model, a permission policy, a different agent spec or role profile, a per-seat runtime or working directory, or startup files. Use rig expand <rig-id> <pod-fragment-path> to add a pod, or rig add <rig-id> <pod-namespace> <member-fragment-path> to add one member to an existing pod.

Don't use this when

  • The rig is being created fresh from scratch — use rig up (lifecycle, not mutation)
  • The intent is to scale specifically (add specialized capacity) — use seat-scaling-and-specialization skill
  • The intent is occupant replacement on a stable seat — use seat-continuity-and-handover skill

Failure modes (4)

  1. A newly created seat lacks the queue, startup context, or role files it needs to operate. Topology mutation creates the seat, but the seat needs more than a tmux session to be useful.
  2. Edges and permissions are not updated when a seat is added or removed. Topology references go stale; later workflows route to nonexistent seats.
  3. Adopt/bind succeeds at the tmux/session layer but not at the OpenRig identity layer. The session is attached but rig whoami doesn't know about it; downstream consumers see partial state.
  4. Shrink/remove leaves stale topology references that later workflows route into. Cleanup is part of the operation, not an afterthought.

Proof standard

Proof must cover mutation while a rig is active, not just in a clean test fixture. The useful matrix:

OperationWhat to verify
Add seatNew seat has queue, startup context, role; rig whoami resolves it
Remove seatStale references cleaned; edges/permissions updated
Adopt existing sessiontmux session bound at OpenRig identity layer; rig whoami reports correctly
Attach observer terminalExternal CLI attachment recorded
Verify topology projections after each moverig ps --nodes reflects current truth, not pre-mutation cache

A clean-fixture proof is necessary but not sufficient. Live-rig proof catches the failure modes that fixture-mode misses.

Stable roles during topology changes

Capacity changes must preserve useful roles, routing and durable work. Distinguish adding or removing a seat from replacing its occupant; use seat-continuity-and-handover for the latter.

Currently shipped surfaces

Per cli-reference.md v0.2.0:

  • rig grow <rig-id> <member...> [--pod <pod> | --new-pod <pod>] [--runtime <runtime>] [--cwd <path>] (added after v0.2.0; check rig grow --help)
  • rig expand <rig-id> <pod-fragment-path> (with optional session_source)
  • rig shrink <rigId> <podRef>
  • rig launch <rigId> <nodeRef>
  • rig remove <rigId> <nodeRef>
  • rig discover [--draft]
  • rig bind <discoveredId> --rig <rigId> (--node <id> | --pod <ns> --member <name>)
  • rig adopt <path> --bind <logicalId=tmuxSessionOrDiscoveryId>
  • rig attach --self --rig <rigId> --node <logicalId>
  • rig unclaim <sessionRef> / rig release <rigId>

Choose the proving environment and authority

Select an isolated active rig or an explicitly authorized live target for the relevant operation. Record its running build, before/after topology, continuity and outstanding work. A passing schema check or isolated fixture does not prove an existing live rig was changed correctly. A runtime timeout is indeterminate until its durable and process effects are reconciled; do not retry blindly.

The matrix above is verification guidance, not permission to modify another rig. Name the operation's owner and scope, preserve the state needed for recovery, and retain missing or failed checks in the result. No local experiment or unfinished proof obligation is implied by loading this skill.

See also

  • openrig-user skill — CLI surface for rig grow / expand / shrink / launch / remove / bind / adopt / attach
  • seat-scaling-and-specialization skill — when to add specialized capacity vs generic
  • seat-continuity-and-handover skill — replacing an occupant on a stable seat (different shape than topology mutation)
  • cross-host-rig-commands skill — cross-host topology mutation (deferred)

Frequently asked questions

What does the Topology Mutation And Seat Management AI skill do?

Use when changing a rig while it is alive — `rig grow` / `rig expand` / `rig shrink` / `rig launch` / `rig remove` / `rig discover` / `rig bind` / `rig adopt` / `rig attach`. Covers the 4 failure modes (newly created seat lacks queue/startup/role; edges and permissions not updated; adopt/bind succeeds at tmux but not OpenRig identity; shrink/remove leaves stale topology references) and the rule that mutation must work while the rig is active, not just in clean fixtures.

Why use Topology Mutation And Seat Management on TypingMind?

Because you install it once and use it with any model. Topology Mutation And Seat Management is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Topology Mutation And Seat Management in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/topology-mutation-and-seat-management. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Topology Mutation And Seat Management?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Topology Mutation And Seat Management?

As many as you like. As long as a model supports skills, you can use Topology Mutation And Seat Management with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Topology Mutation And Seat Management AI skill free?

Yes. It is published on GitHub by mvschwarz under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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