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Rig Bundles And Shareable Artifacts

CommunityPopular
mvschwarz
rig-bundles-and-shareable-artifacts

Use when authoring or installing a rig bundle (packaged, shareable artifact that instantiates an opinionated OpenRig topology + workflow), reasoning about the bundle vs extension boundary, or auditing a bundle for portability. Covers the 4 failure modes that prevent bundles from working anywhere but the operator's machine, and the inspect-before-install discipline.

Overview

Publishermvschwarz
Repositoryopenrig
Skill namerig-bundles-and-shareable-artifacts
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 Rig Bundles And Shareable Artifacts 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/rig-bundles-and-shareable-artifacts
  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/rig-bundles-and-shareable-artifacts .claude/skills/rig-bundles-and-shareable-artifacts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rig Bundles And Shareable Artifacts 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 Bundles And Shareable Artifacts 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 Bundles And Shareable Artifacts 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 Bundles and Shareable Artifacts

A rig bundle is a packaged, shareable artifact that can instantiate an opinionated OpenRig topology and workflow. It may include:

  • rig specs
  • agent specs
  • workflow specs
  • startup files
  • skills or skill references
  • operating-mode declarations
  • proof expectations
  • supporting fragments

Shareable artifact is broader than bundle: specs, bundles, skills, workflows, and extensions can all be shared. A bundle is the packaged form for "load this topology and way of working."

Use this when

  • Authoring a bundle from a proven lab pattern
  • Installing a bundle (rig bundle install <path>)
  • Inspecting a bundle before install (rig bundle inspect <path>)
  • Reasoning about bundle vs extension boundary (bundle declares topology + workflow; extension adds behavior)
  • Auditing a bundle for portability — does it work on a clean OpenRig environment, or only the author's machine?

Don't use this when

  • The work is a one-off topology you won't reuse. Specs alone are sufficient.
  • The intent is to add runtime behavior (commands, views, dashboards). Use extension-and-user-workspace, not bundles.
  • You want to ship a workflow as a daemon feature. Bundle is the path before daemon promotion.

The bundle vs extension boundary (load-bearing)

ConceptDeclaresExample
BundleRig shape + workflow ("load this topology and way of working")A Velocity Team bundle
ExtensionBehavior added to user workspace/runtimeA custom command, view, or dashboard

Don't confuse them. A bundle is opinionated content; an extension is added behavior. Both can be shareable artifacts, but they're structurally distinct.

Failure modes (4)

  1. A bundle works only on the operator's machine because paths, providers, or credentials are implicit. Bundles must be self-describing for a clean OpenRig environment.
  2. A bundle includes too much local state and becomes a backup archive instead of a reusable artifact. Bundle is the intended shape, not the current state of one specific install.
  3. A bundle declares topology but omits proof expectations or workflow mode. Topology alone doesn't tell users what "working as advertised" means.
  4. Users cannot inspect what a bundle will create before installing it. rig bundle inspect must show what will be created without side effects.

Proof standard

Proof should:

  1. Install the bundle into a clean OpenRig environment
  2. Instantiate it
  3. Verify the expected seats and workflow mode
  4. Run a small smoke proof that the topology behaves as advertised

Bundle path → product

Bundles are how proven lab patterns become reusable product experiences:

local dogfood pattern
  → bundle (with manifest, parameterization, proof expectations)
  → installed by another user on clean OpenRig
  → opinionated workflow shipped without becoming a daemon feature
  → if dependable, graduate parts into core daemon

This is also how OpenRig can ship opinionated workflows without making every workflow a daemon feature.

See also

  • extension-and-user-workspace skill — sibling primitive for user-owned behavior added to runtime; bundle vs extension boundary
  • specification-system skill — rig specs / agent specs / workflow specs that bundles package
  • agent-starters skill — bundles can include or reference Agent Starters
  • openrig-user skill — rig bundle create / inspect / install CLI surface

Frequently asked questions

What does the Rig Bundles And Shareable Artifacts AI skill do?

Use when authoring or installing a rig bundle (packaged, shareable artifact that instantiates an opinionated OpenRig topology + workflow), reasoning about the bundle vs extension boundary, or auditing a bundle for portability. Covers the 4 failure modes that prevent bundles from working anywhere but the operator's machine, and the inspect-before-install discipline.

Why use Rig Bundles And Shareable Artifacts on TypingMind?

Because you install it once and use it with any model. Rig Bundles And Shareable Artifacts 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 Bundles And Shareable Artifacts 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-bundles-and-shareable-artifacts. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Rig Bundles And Shareable Artifacts?

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 Bundles And Shareable Artifacts?

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

Is the Rig Bundles And Shareable Artifacts 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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