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K8s Yaml Generator

Community
akin-ozer
k8s-yaml-generator

Generate/create/scaffold Kubernetes YAML — Deployment, Service, ConfigMap, Ingress, RBAC, StatefulSet, CRDs.

Overview

Publisherakin-ozer
Repositorycc-devops-skills
Skill namek8s-yaml-generator
Stars
326
Forks
39
Bundled files
10
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.

  • 10 bundled files

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

  • Open source

    Published by akin-ozer on GitHub. Read the source before you install it.

Installation

Install the K8s Yaml Generator 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/akin-ozer/cc-devops-skills/tree/main/devops-skills-plugin/skills/k8s-yaml-generator
  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/akin-ozer/cc-devops-skills.git /tmp/cc-devops-skills
mkdir -p .claude/skills
cp -r /tmp/cc-devops-skills/devops-skills-plugin/skills/k8s-yaml-generator .claude/skills/k8s-yaml-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable K8s Yaml Generator 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 K8s Yaml Generator 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 K8s Yaml Generator 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.

Kubernetes YAML Generator

Generate Kubernetes manifests with deterministic steps, bounded CRD research, and mandatory validation for full-resource output.

Trigger Guidance

Use this skill when the user asks to create or update Kubernetes YAML, for example:

  • "Generate a Deployment + Service manifest for my app."
  • "Create an Argo CD Application CRD."
  • "Write a StatefulSet with PVC templates."
  • "Produce production-ready Kubernetes YAML with best practices."

Do not use this skill for validation-only requests. For validation-only work, use k8s-yaml-validator.

Execution Model

Normative keywords:

  • MUST: required
  • SHOULD: default unless user requests otherwise
  • MAY: optional

Deterministic sequence:

  1. Preflight request and path/rendering sanity.
  2. Capture minimum required inputs.
  3. Resolve CRD references (bounded workflow only when CRD/custom API is involved).
  4. Generate YAML with baseline quality checks.
  5. Run mandatory validation (or documented fallback path when tooling is unavailable).
  6. Deliver YAML plus explicit validation report and assumptions.

If one step is blocked by environment constraints, execute that step's fallback and continue.

1) Preflight

Before generation:

  • Confirm whether output is full manifest(s) or snippet-only.
  • Confirm target Kubernetes version when provided.
  • Verify any referenced local file path exists before using it.
  • Normalize resource naming to DNS-1123-compatible names where applicable.

Preflight stop condition:

  • If required core inputs are missing (resource type, workload image for Pod-based resources, or CRD kind/apiVersion), ask for those first.

2) Capture Required Inputs

Collect:

  • Resource types (Deployment, Service, ConfigMap, CRD kind, etc.)
  • apiVersion + kind
  • Namespace/scoping requirements
  • Ports, replicas, images, probes, storage, and secret/config needs
  • Environment assumptions (dev/staging/prod)
  • For CRDs: project name and target CRD version if known

Safe defaults (state explicitly in output):

  • Namespace: default (namespace-scoped resources)
  • Deployment replicas: 2
  • Service type: ClusterIP
  • Image pull policy: IfNotPresent (unless user needs forced pulls)

3) CRD Lookup Workflow (Bounded)

Run this step only for custom APIs outside Kubernetes built-in groups.

3.1 Identify CRD target

Extract:

  • API group, version, kind (for example argoproj.io/v1alpha1, Application)
  • Requested product/version (for example Argo CD v2.9.x)

3.2 Context7 primary path

Use the correct Context7 tools and payloads:

  1. mcp__context7__resolve-library-id
  2. mcp__context7__query-docs

Sample payloads:

text
Tool: mcp__context7__resolve-library-id
libraryName: "argo-cd"
query: "Find Argo CD documentation for Application CRD schema compatibility"
text
Tool: mcp__context7__query-docs
libraryId: "/argoproj/argo-cd/v2.9.0"
query: "Application CRD required spec fields for apiVersion argoproj.io/v1alpha1 with minimal valid example"

Selection rules:

  • Prefer exact project/library name matches.
  • Prefer versioned libraryId when user specifies a version.
  • Otherwise use unversioned ID and note version uncertainty.

3.3 Thresholds and stop conditions

Bound the lookup to prevent unbounded retries:

  • resolve-library-id: max 2 attempts (primary name + one alternate name).
  • query-docs: max 3 focused queries total.
  • Web fallback: max 2 version-specific searches.

Stop early when all are true:

  • Required CRD fields are identified.
  • At least one authoritative example is found.
  • Version compatibility is known or explicitly marked unknown.

Hard stop when budgets are exhausted:

  • Generate only fields verified by sources.
  • Mark remaining fields as Needs confirmation.
  • Report residual risk and request one of:
    • exact CRD docs URL, or
    • cluster introspection output (for example kubectl explain <kind>.spec when available).

3.4 Fallback order

Use this order:

  1. Context7 (resolve-library-id -> query-docs)
  2. Official project docs via web search
  3. Cluster-local introspection (kubectl explain, if cluster access exists)

If none are available, provide a minimal, clearly marked draft and do not claim full CRD correctness.

4) YAML Generation Rules

Apply these checks:

  • Use explicit, non-deprecated API versions.
  • Include consistent labels (app.kubernetes.io/*) across related resources.
  • Include namespace for namespace-scoped resources.
  • Add resource requests/limits for Pod workloads unless user opts out.
  • Add readiness/liveness probes for long-running services where applicable.
  • Use securityContext to avoid root execution by default.
  • Keep multi-resource ordering dependency-safe (for example ConfigMap before Deployment consumers).

Minimal label baseline:

yaml
labels:
  app.kubernetes.io/name: myapp
  app.kubernetes.io/instance: myapp-prod
  app.kubernetes.io/part-of: myplatform
  app.kubernetes.io/managed-by: codex

5) Mandatory Validation and Contingencies

For full manifest generation, validation is mandatory.

Primary path:

  • Invoke k8s-yaml-validator.
  • Iterate fix -> revalidate until blocking issues are gone.

Required reporting after each validation pass:

  • Validation mode: k8s-yaml-validator | script fallback | manual fallback
  • Syntax: pass/fail
  • Schema: pass/fail/partial
  • CRD check: pass/fail/partial
  • Dry-run: server/client/skipped
  • Blocking issues remaining: yes/no

Contingency A: validator skill unavailable

Run direct commands:

bash
bash devops-skills-plugin/skills/k8s-yaml-validator/scripts/setup_tools.sh
yamllint -c devops-skills-plugin/skills/k8s-yaml-validator/assets/.yamllint <file.yaml>
kubeconform -schema-location default -strict -ignore-missing-schemas -summary <file.yaml>
server_out="$(mktemp)"
client_out="$(mktemp)"
trap 'rm -f "$server_out" "$client_out"' EXIT

if kubectl apply --dry-run=server -f <file.yaml> >"$server_out" 2>&1; then
  echo "server_validation=passed"
elif grep -Eqi "connection refused|no such host|i/o timeout|tls handshake timeout|unable to connect to the server|no configuration has been provided|the server doesn't have a resource type" "$server_out"; then
  echo "server_validation=skipped"
  if kubectl apply --dry-run=client -f <file.yaml> >"$client_out" 2>&1; then
    echo "client_validation=passed"
  else
    echo "client_validation=failed"
    cat "$client_out"
    exit 1
  fi
else
  echo "server_validation=failed"
  cat "$server_out"
  exit 1
fi

Contingency B: local tools partially unavailable

  • Run available checks.
  • Record skipped checks explicitly.
  • Add residual risk for every skipped check.

Contingency C: repeated validation failure

  • Maximum 3 fix/revalidate cycles.
  • If still failing, stop and return:
    • current YAML,
    • exact failing errors,
    • smallest required user decision/input to unblock.

Validation exceptions:

  • Snippet-only or docs-only requests MAY skip full validation, but the output MUST state Validation status: Skipped (reason).

6) Delivery Contract

Final output MUST include:

  1. Generated YAML.
  2. What was generated (resource list, namespace/scoping).
  3. Validation report in the required format.
  4. Assumptions and defaults used.
  5. References used:
    • Context7 IDs/queries used (for CRDs)
    • external docs/searches used
    • items skipped/missing and impact

Suggested next commands:

bash
kubectl apply -f <filename>.yaml
kubectl get <resource-type> <name> -n <namespace>
kubectl describe <resource-type> <name> -n <namespace>

7) Canonical Example Flows

Example A: Built-in resources (Deployment + Service)

  1. Capture app image, ports, replicas, namespace.
  2. Generate Deployment and Service with consistent labels/selectors.
  3. Validate with k8s-yaml-validator.
  4. Return YAML + validation report + assumptions.

Example B: CRD resource (Argo CD Application)

  1. Extract argoproj.io/v1alpha1 + Application.
  2. Run bounded Context7 lookup (resolve-library-id then query-docs).
  3. If needed, perform bounded web fallback.
  4. Generate CRD YAML only with verified fields.
  5. Validate, report any partial verification, and return residual risks.

8) Definition of Done

Execution is complete only when all applicable checks pass:

  • Trigger use case is correct (generation, not validation-only).
  • Required inputs are captured or explicit assumptions are documented.
  • CRD lookup follows bounded thresholds and stop conditions.
  • Tool names and command paths are valid and consistent.
  • Full manifests are validated (or fallback path is documented with residual risk).
  • Final response includes YAML, validation report, assumptions, and references.

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 K8s Yaml Generator AI skill do?

Generate/create/scaffold Kubernetes YAML — Deployment, Service, ConfigMap, Ingress, RBAC, StatefulSet, CRDs.

Why use K8s Yaml Generator on TypingMind?

Because you install it once and use it with any model. K8s Yaml Generator 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 K8s Yaml Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/akin-ozer/cc-devops-skills/tree/main/devops-skills-plugin/skills/k8s-yaml-generator. 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 K8s Yaml Generator?

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 K8s Yaml Generator?

As many as you like. As long as a model supports skills, you can use K8s Yaml Generator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the K8s Yaml Generator AI skill free?

Yes. It is published on GitHub by akin-ozer 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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