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rig-bundles-and-shareable-artifacts

Package an OpenRig agent arrangement and workflow for reuse by others. Guides bundle creation, inspection before installation, and checks in a clean environment to identify hidden dependencies on local paths, providers, or credentials.

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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.

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适用场景

  • Package a proven agent setup for reuse
  • Inspect bundle contents before installation
  • Check whether a bundle works in a clean environment
  • Decide whether to use a bundle or an extension

包含的文件(1)

  • SKILL.md4.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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

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