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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
Vue d’ensemble
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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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)
| Concept | Declares | Example |
|---|---|---|
| Bundle | Rig shape + workflow ("load this topology and way of working") | A Velocity Team bundle |
| Extension | Behavior added to user workspace/runtime | A 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)
- 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.
- 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.
- A bundle declares topology but omits proof expectations or workflow mode. Topology alone doesn't tell users what "working as advertised" means.
- Users cannot inspect what a bundle will create before installing it.
rig bundle inspectmust show what will be created without side effects.
Proof standard
Proof should:
- Install the bundle into a clean OpenRig environment
- Instantiate it
- Verify the expected seats and workflow mode
- 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-workspaceskill — sibling primitive for user-owned behavior added to runtime; bundle vs extension boundaryspecification-systemskill — rig specs / agent specs / workflow specs that bundles packageagent-startersskill — bundles can include or reference Agent Starterscomposable-priming-packsskill — bundles can package priming packsopenrig-userskill —rig bundle create / inspect / installCLI surface
Métadonnées du fichier
name: rig-bundles-and-shareable-artifacts
description: 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.
metadata:
cli_surfaces_referenced:
- bundle create
- bundle inspect
- bundle install
openrig:
stage: factory-approved
sibling_skills:
- rig-lifecycle
- topology-mutation-and-seat-management
- seat-scaling-and-specialization
- cross-host-rig-commands
- sidecar-operator
- specification-system
- extension-and-user-workspaceVoir le texte original
---
name: rig-bundles-and-shareable-artifacts
description: 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.
metadata:
cli_surfaces_referenced:
- bundle create
- bundle inspect
- bundle install
openrig:
stage: factory-approved
sibling_skills:
- rig-lifecycle
- topology-mutation-and-seat-management
- seat-scaling-and-specialization
- cross-host-rig-commands
- sidecar-operator
- specification-system
- extension-and-user-workspace
---
# 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)
| Concept | Declares | Example |
|---|---|---|
| **Bundle** | Rig shape + workflow ("load this topology and way of working") | A Velocity Team bundle |
| **Extension** | Behavior added to user workspace/runtime | A 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
- `composable-priming-packs` skill — bundles can package priming packs
- `openrig-user` skill — `rig bundle create / inspect / install` CLI surface
Examiner la source
Prix et coûts d’utilisation
- Obtenir le skill
- Prix non confirmé
- L’utiliser
- Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
- Licence
- Apache-2.0
- Prix non confirmé
- Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.
Gratuit à obtenir ne signifie pas gratuit à utiliser. Le prix ne constitue pas une évaluation de sécurité. Soumettre un prix →
Source du skill enregistrée
Un chemin vers les instructions est enregistré. Cela ne constitue pas un test, une garantie de sécurité ou de compatibilité.
Réviser avant installation: Éviter l’installation automatique
Licence: Apache-2.0
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- L’approbation de revue IA est absente
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.
Commencer par une petite tâche
- 1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
- 2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
- 3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.
Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.
Source et conseils d’utilisation
Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.
- Dépôt source
- mvschwarz/openrig
- Licence
- Apache-2.0
- Version
- Unknown
- Dernier push GitHub
- 27 sept. 2026
- Registre mis à jour
- 27 sept. 2026
Version déclarée dans le registre ; vérifiez les versions de la source.
Qualité
69/100
Prometteur
Confiance
67/100
Sandbox uniquement
Audit
78/100
Revue nécessaire
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- L’approbation de revue IA est absente
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
- Verified installs
- —
- Résultats
- —
Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.
Accès agent
L’API Registry fournit les signaux de décision, confiance, audit, cas d’usage et installation sans analyser l’interface.
Plus de détails
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"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20rig-bundles-and-shareable-artifacts%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20rig-bundles-and-shareable-artifacts%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/mvschwarz-rig-bundles-and-shareable-artifacts/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/mvschwarz-rig-bundles-and-shareable-artifacts"
}
}Pour le créateur
Source de la fiche
Indexé par Registry
Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.
- Créateur
- mvschwarz
- Source
- mvschwarz/openrig
- Indexé par
- Index communautaire OpenAgentSkill
L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.
Revendiquer ce skillRevendication du propriétaire
Revendiquer cette fiche de skill
Cette fiche Indexé par Registry est attribuée à mvschwarz, mais n’est pas encore marquée officielle. Revendiquez-la pour ajouter un signal de propriétaire vérifié et rendre les futures mises à jour de lancement, d’installation et d’audit plus fiables.
Kit de partage
Kit de backlinks créateur
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Affichez la fiche canonique, les signaux actuels de confiance et d’audit, ainsi que de vraies preuves Agent-Proven là où les développeurs évaluent le dépôt.
[](https://www.openagentskill.com/skills/mvschwarz-rig-bundles-and-shareable-artifacts?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/mvschwarz-rig-bundles-and-shareable-artifacts?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/mvschwarz-rig-bundles-and-shareable-artifacts/audit)
[](https://www.openagentskill.com/skills/mvschwarz-rig-bundles-and-shareable-artifacts?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)Signal de communauté
Indiquez si ce skill semble utile à votre workflow Agent. Les retours agrégés améliorent le classement au fil du temps.
