Rig Lifecycle

CommunityPopular

mvschwarz

rig-lifecycle

Use when reasoning about the rig lifecycle operations family (create / start / stop / resume / restore / snapshot / release / unclaim / destroy), reading or trusting `rig ps` / lifecycle projections after recovery, or designing proof for a lifecycle scenario. Covers the 4 failure modes (auto-restore creates partial rig; projections report healthier than reality; provider auth treated as impl work; resume succeeds for one runtime fails another) plus the restore-honesty rule (failed resume is FAILED loudly — no auto fresh fallback).

Overview

Publisher	mvschwarz
Repository	`openrig`
Skill name	`rig-lifecycle`
Stars	5K
Forks	366
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository Homepage

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 Rig Lifecycle AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/rig-lifecycle
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/core/rig-lifecycle

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/rig-lifecycle .claude/skills/rig-lifecycle

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rig Lifecycle 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 Rig Lifecycle 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 Rig Lifecycle 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.

Rig Lifecycle

The family of operations that create, start, stop, resume, restore, snapshot, release, unclaim, and destroy OpenRig-managed topologies. Includes the user story after reboot: "bring my work back without turning a clean recovery into a cleanup project."

If lifecycle is brittle, every higher-level primitive inherits that brittleness. Queue, workflow, seat continuity, cross-host operation, and RSI all assume that rigs and seats can be restored into known states.

Use this when

Operating rig up / down / restore / resume / snapshot / release / unclaim / destroy
Reading rig ps after a reboot or recovery and deciding what to trust
Designing proof for a lifecycle scenario (clean start / warm resume / host reboot / provider auth loss / partial boot / operator recovery)
Reasoning about restore-outcome semantics (resumed / rebuilt / fresh / failed / attention_required)
Auditing whether a lifecycle proof is in-process bedrock vs requires real reboot evidence

Don't use this when

The operation is single-command and deterministic — use openrig-user skill for CLI surface
The work is operator-level configuration of OpenRig itself — use openrig-operator
The work is rig spec authoring — use openrig-architect

Failure modes (4)

Auto-restore creates a partially restored rig that must be cleaned up before real recovery. Partial restore looks like recovery but isn't; cleanup-before-recovery becomes the actual workload.
rig ps or lifecycle projections report a healthier state than the runtime actually has. Projections are summaries; the runtime is truth. Don't trust projections silently.
Provider auth is unavailable after reboot and the system treats that as implementation work instead of a human/environment decision. Auth issues are environmental; route to human.
Resume succeeds for one runtime/provider but fails for another scenario that was never tested. Per-runtime parity assumptions break silently; matrix proof catches them.

Restore-honesty rule (load-bearing)

Failed resume is FAILED loudly. No automatic fresh fallback. Fresh launch is explicit follow-up only. This is enforced architecturally at the daemon level (per architecture.md §7 rule 15).

The locked restore-outcome vocabulary:

Outcome	Meaning
`resumed`	Native runtime resumed the same conversation
`rebuilt`	New process assembled from artifacts (`session_source: mode: rebuild`)
`fresh`	New process with no prior continuity
`failed`	Restore attempted and failed; no automatic fallback
`attention_required`	Recoverable blocker (provider auth refused, etc.); needs operator action
`n-a`	Not applicable (terminal nodes, etc.)

Codex auth-refusal returns attention_required (recoverable); Claude looksLikeClaudeLoginPrompt returns failed/login_required (terminal). Cross-runtime alignment is an open follow-up question.

Proof standard

Lifecycle proof should include real reboot or VM-reboot evidence, not only daemon-unit evidence. The minimum useful matrix covers:

Scenario	What it proves
Clean start	Boot from spec into known state
Warm resume	`rig down` → `rig up <name>` resumes seats
Host reboot / tmux socket absence	Recovery from lost tmux connection
Provider auth loss	Codex/Claude auth refusal handled honestly
Partial boot / partial failure	Some seats up, some failed; honest reporting
Intentional operator recovery	Operator-initiated restore from snapshot

Tier 1: in-process bedrock (daemon-unit evidence). Tier 2: real reboot or disposable Tart VM. Tier 1 alone is not lifecycle proof; it's bedrock.

Default policy is part of the primitive

Treat default policy as part of the primitive, not an afterthought. Lifecycle defaults that are too optimistic for the reliability level actually proven create silent harm:

auto-restore defaults that mask failure modes
Implicit fresh-fallback that hides resume failure
Permissive verification that doesn't distinguish honest success from "appeared to work"

Total-Host Restore Product Rail v2

The rail sequences restore truth into four rungs:

fully_restored is a Rung 3 execution rollup
"fully back" is reserved for Rung 4 only

Naming discipline matters. "Fully restored" and "fully back" are different claims; don't conflate.

Frequently asked questions

What does the Rig Lifecycle AI skill do?

Why use Rig Lifecycle on TypingMind?

Because you install it once and use it with any model. Rig Lifecycle 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 Rig Lifecycle 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/rig-lifecycle. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Rig Lifecycle?

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 Rig Lifecycle?

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

Is the Rig Lifecycle 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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Rig Lifecycle

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Rig Lifecycle

Use this when

Don't use this when

Failure modes (4)

Restore-honesty rule (load-bearing)

Proof standard

Default policy is part of the primitive

Total-Host Restore Product Rail v2

See also

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Rig Lifecycle

Use this when

Don't use this when

Failure modes (4)

Restore-honesty rule (load-bearing)

Proof standard

Default policy is part of the primitive

Total-Host Restore Product Rail v2

See also

Frequently asked questions

Related AI Skills

Set up your own AI workspace now