Agent skill

Castai Reference Architecture

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Design a CAST AI reference architecture that separates hosted control-plane services, in-cluster components, cloud permissions, delivery ownership, and Kubernetes scaling controls.

MITAuto-check passedDevOps & Cloud

Install Castai Reference Architecture

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill castai-reference-architecture -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace castai-reference-architecture --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/castai-reference-architecture .claude/skills/castai-reference-architecture && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
castai-reference-architecture
GitHub stars
2.8k
Token cost
~1.2k tokens
SKILL.md length
447 words
Files
2 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Design a CAST AI reference architecture that separates hosted control-plane services, in-cluster components, cloud permissions, delivery ownership, and Kubernetes scaling controls.

  • Works in 6 steps: Establish trust zones → Map in-cluster components by mode → Map control paths → …
  • Reviewing architecture
  • SKILL.md covers Overview, Prerequisites, Instructions and Tool Discipline, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Castai Reference Architecture is an agent skill from jeremylongshore/tons-of-skills-marketplace. Design a CAST AI reference architecture that separates hosted control-plane services, in-cluster components, cloud permissions, delivery ownership, and Kubernetes scaling controls. Use when reviewing architecture, planning multi-cluster rollout, or mapping responsibilities. Trigger with: "design CAST AI architecture", "map CAST AI components", "review CAST AI control planes".

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/official-docs.md`). Compatibility notes: Covers documented CAST AI connection and umbrella-chart modes; exact components and permissions depend on selected features and provider

It sits in DevOps & Cloud, covering Container orchestration. It works with Kubernetes. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Reviewing architecture
  • Planning multi-cluster rollout
  • Mapping responsibilities
  • With: design CAST AI architecture

Example prompts

  • “design CAST AI architecture”
  • “map CAST AI components”
  • “review CAST AI control planes”
  • “/castai-reference-architecture”

Requirements

  • Compatibility (from SKILL.md): Covers documented CAST AI connection and umbrella-chart modes; exact components and permissions depend on selected features and provider
  • Pre-approved tools (allowed-tools): Read, Grep, Write, Edit

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Establish trust zones
  2. Map in-cluster components by mode
  3. Map control paths
  4. Map permission boundaries
  5. Map configuration authority
  6. Add failure and rollback paths

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Grep
    • Write
    • Edit

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.cast.ai

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Covers documented CAST AI connection and umbrella-chart modes; exact components and permissions depend on selected features and provider

    From compatibility in the SKILL.md frontmatter.

Context cost

Castai Reference Architecture loads about 1.2k tokens when it runs, and up to ~1.4k if it reads all its reference files. Until then it costs about 102 tokens; SKILL.md has 447 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~102
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 447 words, ~1,187 tokens.

Download SKILL.mdSave it as .claude/skills/castai-reference-architecture/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
castai-reference-architecture
description
Design a CAST AI reference architecture that separates hosted control-plane services, in-cluster components, cloud permissions, delivery ownership, and Kubernetes scaling controls. Use when reviewing architecture, planning multi-cluster rollout, or mapping responsibilities. Trigger with: "design CAST AI architecture", "map CAST AI components", "review CAST AI control planes".
allowed-tools
Read, Grep, Write, Edit
compatibility
Covers documented CAST AI connection and umbrella-chart modes; exact components and permissions depend on selected features and provider
version
2.0.0
argument-hint
[estate-or-cluster-scope]
model
inherit
effort
high
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, kubernetes, cast-ai, architecture, governance

CAST AI Reference Architecture

Overview

Describe the real control and data paths for one estate. Avoid a generic vendor diagram: show where identity, telemetry, recommendations, mutations, cloud provisioning, GitOps, and operator approvals actually cross boundaries.

Prerequisites

  • Cluster inventory, providers, regions, network zones, and CAST AI organizations
  • Selected modes: read-only, Workload Autoscaler, Node Autoscaler, or full
  • IaC/GitOps ownership, existing provisioners, HPAs, PDBs, and data policy

Instructions

Step 1: Establish trust zones

Use Read and Grep to map CAST AI hosted services, regional API base, enterprise or organization identity, cloud accounts, Kubernetes API servers, CI, GitOps controllers, secret managers, and operator workstations.

Step 2: Map in-cluster components by mode

Represent the unified umbrella chart and its selected tag mode. Shared components include the agent, spot handler, and Kvisor; automation modes add controllers, evictor, pod mutator, workload autoscaler/exporter, pod pinner, or live-migration components as documented. Do not show every component as present unconditionally.

Step 3: Map control paths

Separate observation snapshots, cost telemetry, workload recommendations, mutating admission, Eviction API operations, native HPA management, pending-pod observation, node provisioning, and cloud API calls. Label which paths read, recommend, mutate, or provision.

Step 4: Map permission boundaries

Show human castctl login, service API keys, enterprise child-organization targeting, Kubernetes service accounts, cloud IAM, and optional Kvisor capabilities. Link each privilege to a feature and identify the revocation owner.

Step 5: Map configuration authority

Use Write or Edit to declare whether castctl, Terraform, Helm GitOps, console, or workload annotations own each setting. Highlight conflicts with Karpenter, cloud autoscalers, native HPAs, and manual console changes.

Show full SKILL.md (191 more words)Show less
Step 6: Add failure and rollback paths

Include regional API loss, agent disconnect, missing metrics, webhook failure, exhausted cloud quota, unsatisfied node template, PDB denial, and bad policy rollout. Show observation continuity, fail-safe behavior, escalation, and rollback authority.

Tool Discipline

Use Read and Grep for repository and environment evidence. Use Write and Edit for the architecture record and diagram source. Do not infer selected features, components, permissions, or network reachability from a default installation.

Output

  • Trust-zone and component view
  • Read/recommend/mutate/provision data-flow view
  • Identity, permission, and configuration-authority matrix
  • Failure, rollback, and escalation paths

Examples

A read-only cluster shows telemetry components but no node provisioner. A full-mode cluster shows CAST AI workload recommendations and node provisioning while GitOps remains the sole authority for chart values and policy definitions.

Error Handling

FailureResponse
Selected umbrella mode is unknownMark components unresolved and inspect the release
Two systems own the same controlSurface an architecture decision, not a silent merge
Network path is assumedLabel it unverified until tested
Permission cannot be tied to a featureTreat it as excess privilege for review

Resources

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/.curated/castai-reference-architecture of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/official-docs.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

Castai Reference Architecture next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Castai Reference Architecture compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Castai Reference Architecture this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1.2kAutomated safety check: PassMIT
Kubeshark Installerkubeshark/kubeshark12k—~3.6kAutomated safety check: NotesApache-2.0
KubeSphere Multi-Tenant Managementkubesphere/kubesphere17k—~3.1kAutomated safety check: PassCustom licence
Sim Helmsimstudioai/sim30k—~2.2kAutomated safety check: PassApache-2.0
Helm Chart ScaffoldingCybereason-Public/owLSM28013 repos~381Automated safety check: PassGPL-2.0
Mirrord Operatormetalbear-co/mirrord5.4k1 repos~4.6kAutomated safety check: PassMIT

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Works with

Categories

Questions about Castai Reference Architecture

What does Castai Reference Architecture do?

Design a CAST AI reference architecture that separates hosted control-plane services, in-cluster components, cloud permissions, delivery ownership, and Kubernetes scaling controls. Castai Reference Architecture is an agent skill from jeremylongshore/tons-of-skills-marketplace. Design a CAST AI reference architecture that separates hosted control-plane services, in-cluster components, cloud permissions, delivery ownership, and Kubernetes scaling controls.

When should I use Castai Reference Architecture?

Castai Reference Architecture fits situations like: reviewing architecture; planning multi-cluster rollout; mapping responsibilities; with: design CAST AI architecture.

How do I install Castai Reference Architecture in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill castai-reference-architecture -a claude-code`. Or copy the skill folder (skills/.curated/castai-reference-architecture in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/castai-reference-architecture in your project. Claude Code loads it when a task matches its description.

How do I install Castai Reference Architecture in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill castai-reference-architecture -a codex`. Or copy the skill folder (skills/.curated/castai-reference-architecture in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/castai-reference-architecture in your project. Codex loads it when a task matches its description.

Can I use Castai Reference Architecture in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill castai-reference-architecture -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/castai-reference-architecture, .gemini/skills/castai-reference-architecture, .github/skills/castai-reference-architecture and .opencode/skills/castai-reference-architecture in your project.

What does Castai Reference Architecture need to run?

SKILL.md names no scripts, command-line tools or credentials: Castai Reference Architecture is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Grep, Write, Edit. Compatibility (from SKILL.md): Covers documented CAST AI connection and umbrella-chart modes; exact components and permissions depend on selected features and provider.

Does Castai Reference Architecture access the network?

SKILL.md names 1 domain. As links in the text: docs.cast.ai. This is read from the text; nothing was executed.

Is Castai Reference Architecture safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Castai Reference Architecture use?

Castai Reference Architecture is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Castai Reference Architecture use?

About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 261 tokens, read only when the agent opens those files.

What are the alternatives to Castai Reference Architecture?

Skills that share tags, products or a category with Castai Reference Architecture: Kubeshark Installer (kubeshark/kubeshark, 12k stars), KubeSphere Multi-Tenant Management (kubesphere/kubesphere, 17k stars), Sim Helm (simstudioai/sim, 30k stars) and Helm Chart Scaffolding (Cybereason-Public/owLSM, 280 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Castai Reference Architecture?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.