
k8s-yaml-generator
Generate/create/scaffold Kubernetes YAML — Deployment, Service, ConfigMap, Ingress, RBAC, StatefulSet, CRDs.
- Installs
- 656
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/akin-ozer/cc-devops-skills --skill k8s-yaml-generator- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads k8s-yaml-generator automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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: requiredSHOULD: default unless user requests otherwiseMAY: optional
Deterministic sequence:
- Preflight request and path/rendering sanity.
- Capture minimum required inputs.
- Resolve CRD references (bounded workflow only when CRD/custom API is involved).
- Generate YAML with baseline quality checks.
- Run mandatory validation (or documented fallback path when tooling is unavailable).
- 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:
mcp__context7__resolve-library-idmcp__context7__query-docs
Sample payloads:
Tool: mcp__context7__resolve-library-id
libraryName: "argo-cd"
query: "Find Argo CD documentation for Application CRD schema compatibility"
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
libraryIdwhen 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>.specwhen available).
3.4 Fallback order
Use this order:
- Context7 (
resolve-library-id->query-docs) - Official project docs via web search
- 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
securityContextto avoid root execution by default. - Keep multi-resource ordering dependency-safe (for example ConfigMap before Deployment consumers).
Minimal label baseline:
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 fallbackSyntax: pass/failSchema: pass/fail/partialCRD check: pass/fail/partialDry-run: server/client/skippedBlocking issues remaining: yes/no
Contingency A: validator skill unavailable
Run direct commands:
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:
- Generated YAML.
- What was generated (resource list, namespace/scoping).
- Validation report in the required format.
- Assumptions and defaults used.
- References used:
- Context7 IDs/queries used (for CRDs)
- external docs/searches used
- items skipped/missing and impact
Suggested next commands:
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)
- Capture app image, ports, replicas, namespace.
- Generate Deployment and Service with consistent labels/selectors.
- Validate with
k8s-yaml-validator. - Return YAML + validation report + assumptions.
Example B: CRD resource (Argo CD Application)
- Extract
argoproj.io/v1alpha1+Application. - Run bounded Context7 lookup (
resolve-library-idthenquery-docs). - If needed, perform bounded web fallback.
- Generate CRD YAML only with verified fields.
- 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.
FAQ
What does the k8s-yaml-generator skill do?
Generate/create/scaffold Kubernetes YAML — Deployment, Service, ConfigMap, Ingress, RBAC, StatefulSet, CRDs.
How do I install k8s-yaml-generator?
Run npx skills add https://github.com/akin-ozer/cc-devops-skills --skill k8s-yaml-generator in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/k8s-yaml-generator/ (personal) or .claude/skills/k8s-yaml-generator/ (project).
Which AI agents can use k8s-yaml-generator?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is k8s-yaml-generator safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains k8s-yaml-generator?
It is published by akin-ozer in akin-ozer/cc-devops-skills. It has 656 recorded installs.
More from akin-ozer/cc-devops-skills
ansible-validator
akin-ozer/cc-devops-skills
Validate, lint, audit, or debug Ansible playbooks, roles, inventories, FQCN, tasks.
terragrunt-generator
akin-ozer/cc-devops-skills
Generate/create/scaffold Terragrunt HCL files — root.hcl, terragrunt.hcl, child modules, stacks, multi-env layouts.
k8s-debug
akin-ozer/cc-devops-skills
Diagnose and fix Kubernetes pods, CrashLoopBackOff, Pending, DNS, networking, storage, and rollout failures with kubectl.
ansible-generator
akin-ozer/cc-devops-skills
Generate, create, or scaffold Ansible playbooks, roles, tasks, handlers, inventory, vars.
jenkinsfile-generator
akin-ozer/cc-devops-skills
Generate/create/scaffold Jenkinsfile — declarative, scripted, shared library, CI/CD pipelines.
dockerfile-validator
akin-ozer/cc-devops-skills
Validate, lint, audit, or scan a Dockerfile for security and best practices.
Popular devops & cloud skills
microsoft-foundry
microsoft/azure-skills
Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure
azure-diagnostics
microsoft/azure-skills
Debug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, container app 503 or target port mismatch, troubleshoot functions, function telemetry missing or stopped after a config/connection-string change, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, analyze logs, KQL, insights, image pull failures, cold start issues, health probe failures, resource health, root cause of errors, troubleshoot event hubs, troubleshoot service bus, messaging SDK error, AMQP connection failure, message lock lost, lock renewal during long-running processing, service bus dead letter.
azure-prepare
microsoft/azure-skills
Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azd infrastructure, modernize app for Azure with azd, deploy with azd, function app, timer trigger, service bus trigger, event-driven function, managed identity, generate Bicep, generate Terraform, create and deploy to Azure.
azure-ai
microsoft/azure-skills
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
azure-deploy
microsoft/azure-skills
Execute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: "run azd up", "run azd deploy", "execute deployment", "push to production", "push to cloud", "go live", "ship it", "bicep deploy", "terraform apply", "publish to Azure", "launch on Azure". DO NOT USE WHEN: "create and deploy", "build and deploy", "create a new app", "set up infrastructure", "create and deploy to Azure using Terraform" — use azure-prepare for these.
azure-validate
microsoft/azure-skills
Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.