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Openbkn Deploy

Organization
openbkn-ai
openbkn-deploy

Deploy or upgrade OpenBKN on a customer-authorized Linux server through the repository's deploy scripts, with preflight checks, explicit confirmation, secret handling, and post-deployment verification.

Overview

Publisheropenbkn-ai
Repositorybkn-foundry
Skill nameopenbkn-deploy
Stars
664
Forks
56
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by openbkn-ai on GitHub. Read the source before you install it.

Installation

Install the Openbkn Deploy 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/openbkn-ai/bkn-foundry/tree/main/deploy/openbkn-deploy
  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/openbkn-ai/bkn-foundry.git /tmp/bkn-foundry
mkdir -p .claude/skills
cp -r /tmp/bkn-foundry/deploy/openbkn-deploy .claude/skills/openbkn-deploy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openbkn Deploy 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 Openbkn Deploy 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 Openbkn Deploy 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.

OpenBKN deployment

Use this skill when a customer asks to install, upgrade, verify, or diagnose an OpenBKN deployment using this repository's deploy/ directory.

Scope and authorization

  • Act only on a server the customer explicitly identifies and authorizes.
  • Before any remote mutation, show the target host, SSH user, selected version, deployment mode, components, and expected impact; require explicit confirmation.
  • Never assume that providing credentials authorizes an upgrade, data cleanup, reset, uninstall, or production change.
  • Never put passwords, private keys, tokens, or generated credentials in this skill, Git, command arguments, shell history, or normal logs. Prefer an SSH key or a temporary credential supplied through a secure secret mechanism.
  • Never run k8s reset, delete PVCs, uninstall data services, run trace cleanup, or perform a database/schema migration without a separate confirmation that states what changes, blast radius, and rollback/backup plan.

Standard workflow

  1. Collect only the required deployment inputs: target host, SSH method, sudo/root access, access address, Kubernetes distribution (k8s or k3s), requested version, registry, whether this is a new install or upgrade, the delivery source (release package or repository URL), and the deployment directory if it already exists.

  2. Inspect the target without changing it: OS, CPU, memory, disk, hostname, time sync, open ports, container runtime, Kubernetes context, kubectl, and helm availability.

  3. Locate and verify the deployment directory. Prefer the directory supplied by the customer. If absent, do a limited read-only search for a directory containing deploy/deploy.sh; if there are zero or multiple candidates, stop and ask the customer to identify the intended directory. Verify the selected directory's source and version before using it.

    • Establish one canonical deployment root for the entire operation. Once selected or created, reuse it for every preflight, manifest, download, and deploy command.
    • Never create a parallel directory such as /opt/bkn-foundry-<version> to resolve a version mismatch. If the selected root's source, commit/tag, VERSION, or release manifest does not match the requested release, stop and report the mismatch; replace it only with explicit customer approval or use an explicitly supplied release package in the same approved root.
    • The deployment script, VERSION, release manifest, and charts must come from the same verified source/tag. Record the canonical root, source URL, commit/tag, script version, and manifest path before mutation.
  4. If no verified deployment directory exists, obtain the customer-approved release package or clone the approved repository at the requested tag/commit. If Git is unavailable, use the release package; do not install Git or download an unapproved package without confirmation. Work from the resulting deploy/ directory.

  5. Run the repository preflight in check-only mode first. When the customer confirms host preparation, the default deployment policy is to disable the host firewall; state this explicitly in the confirmation, identify the detected firewall implementation, and report the change after it is made. Do not change cloud security groups or external network firewalls without separate authorization.

  6. For a new install, use the pinned release manifest when one is requested or available. Prefer the repository's documented version entrypoint (for example, --version=<version>) when it resolves the same verified manifest; use --version_file only when needed and record why. Do not use --latest in production unless explicitly authorized. Do not mix a script from one source/tag with a manifest or charts from another.

    • If the target has less than 8 GiB total memory or fewer than 8 logical CPU cores, append both --set resources.requests.cpu=0m and --set resources.requests.memory=0Mi to the openbkn install command, unless the customer explicitly supplied different resource overrides. Report this low-resource override as a lab-only warning; it does not make the host meet the recommended production capacity.
    • Default/latest mode is distinct: when the customer explicitly chooses the default or omits both version and version_file, the repository's documented behavior is to use the official main branch and let the script resolve/install the newest available release. Omit both version flags, state that this is a moving-target latest install, and record the resolved version after installation. This is allowed only with explicit customer authorization; never silently substitute it for a requested pinned release.
  7. For an upgrade, verify backups, current Helm releases, PVCs, runtime configuration, current version, and release notes. Read the applicable upgrade instructions before running any migration or cleanup script.

  8. Execute the existing deployment scripts rather than reimplementing Helm commands. Do not run onboard.sh as part of the default installation; leave post-install business initialization to the customer as a separate, explicitly requested operation:

    bash
    sudo bash ./preflight.sh --check-only
    sudo bash ./deploy.sh openbkn install \
      --registry=swr \
      --access_address=https://HOST \
      --api_server_address=API_IP
    sudo bash ./deploy.sh openbkn status
  9. Verify Kubernetes nodes, Pods, Helm releases, Ingress, service health, access URL, and the recorded runtime configuration. Report failures with the exact component and next safe diagnostic command.

Upgrade rules

  • Treat git pull as a code/config change: inspect the diff and identify the target version before installing.
  • Use --version_file=./release-manifests/<version>/bkn-foundry.yaml for reproducible production upgrades.
  • Do not run legacy Trace cleanup merely because the document mentions it; confirm that the target upgrade requires it, take a backup, quiesce writes, and obtain explicit confirmation.
  • Do not assume changing a ConfigMap or OPENSEARCH_INITIAL_ADMIN_PASSWORD changes an already-initialized OpenSearch password on an existing PVC. Distinguish application connection settings from the OpenSearch internal admin credential.
  • Do not run onboard.sh unless the customer separately requests post-install initialization such as model registration, business-user provisioning, or CLI login setup.
  • Use --force-upgrade only when a deliberate configuration or access-address change requires it, and explain the expected rollout.

Failure handling

  • Stop on authentication, target mismatch, backup failure, ambiguous version, or unexpected destructive prompt.
  • Diagnose with read-only commands first (kubectl get, kubectl describe, kubectl logs, helm list, helm status).
  • Do not “fix” failures by deleting Pods, PVCs, namespaces, or the cluster unless the customer separately confirms the exact action.
  • At completion, provide the deployed version, access URL, admin credential delivery location (never print the secret), health summary, commands run, and remaining warnings.

For detailed command selection and confirmation wording, read references/operations.md.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Openbkn Deploy AI skill do?

Deploy or upgrade OpenBKN on a customer-authorized Linux server through the repository's deploy scripts, with preflight checks, explicit confirmation, secret handling, and post-deployment verification.

Why use Openbkn Deploy on TypingMind?

Because you install it once and use it with any model. Openbkn Deploy 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 Openbkn Deploy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openbkn-ai/bkn-foundry/tree/main/deploy/openbkn-deploy. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Openbkn Deploy?

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 Openbkn Deploy?

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

Is the Openbkn Deploy AI skill free?

It is published on GitHub by openbkn-ai. Check the repository for licensing terms. 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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