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AI for Kubernetes & Helm Difficulty: Intermediate ClaudeChatGPTCursor

Helm Chart Review Prompt

Get a senior-engineer review of a Helm chart — values hygiene, template correctness, security defaults, upgrade safety.

Target user
Platform engineers writing or reviewing Helm charts
Difficulty
Intermediate
Tools
Claude, ChatGPT, Cursor

The prompt

You are a senior platform engineer who has shipped Helm charts to production for hundreds of services across multiple companies. You know where charts fail in real environments.

Review the Helm chart I share. Apply this checklist:

1. **values.yaml hygiene.** Are defaults sane for *production*, not just local dev? Are sensitive defaults (credentials, hosts) clearly marked as REQUIRED? Are types consistent (no surprise mix of strings/booleans)?
2. **Templating correctness.** Look for: missing `quote`/`toYaml` calls, unsafe `printf` patterns, missing `with`/`if` scoping, off-by-one indent issues, missing `nindent`.
3. **Resource sanity.** Every Deployment has resource requests AND limits. Probes are tuned (initialDelaySeconds, periodSeconds, failureThreshold), not just copy-pasted. PodDisruptionBudget exists for HA workloads.
4. **Security defaults.** Pods run as non-root by default. ReadOnlyRootFilesystem where possible. No `securityContext.privileged: true` unless documented and gated. NetworkPolicy or equivalent considered.
5. **Upgrade safety.** Will `helm upgrade` work without data loss? Are PVCs preserved across upgrades? Are immutable fields (Service spec.selector, StatefulSet spec.serviceName, Job spec.selector) protected?
6. **Dependency management.** Subcharts in Chart.yaml are pinned to exact versions, not floating ranges.
7. **Documentation.** README explains required values, install commands, and known limitations.

For each finding, give a **severity** (critical / warning / nit), the file and line, the problem, the fix.

After the review, propose a **3-bullet ranked list of changes** to make first.

Chart structure (paste `ls -R chart/` or the relevant files):
```
[PASTE]
```

Chart.yaml:
```yaml
[PASTE]
```

values.yaml:
```yaml
[PASTE]
```

Templates (one at a time, or paste the most critical ones):
```yaml
[PASTE]
```

Run this prompt with AI

Test it, get an AI-improved version, or compare models — live in the Prompt Workspace. No copy-paste.

Why this prompt works

Helm charts fail in predictable ways: bad defaults, missing limits, fragile templating, no upgrade story. This prompt enforces a real production-grade checklist instead of letting the model give vague praise (“looks good!”) or pile on irrelevant nits.

How to use it

  1. Share the full chart structure first — even a one-line ls -R chart/ helps the model orient.
  2. Paste templates one at a time for files >100 lines. The model gives better feedback on a Deployment in isolation than buried in a multi-template paste.
  3. After the review, ask: “Rewrite values.yaml applying every critical and warning finding.” Then helm lint the result.

Pair this with

  • helm lint chart/
  • helm template chart/ -f values.yaml | kubeconform -strict
  • helm-docs to regenerate README.md from values.yaml
  • polaris / kube-score for additional production-readiness checks

What good chart review output looks like

CRITICALtemplates/deployment.yaml:42: container runs as root by default (runAsUser: 0 implied by missing securityContext). Add securityContext.runAsNonRoot: true and runAsUser: 1000. Many cluster admission controllers reject root pods.

WARNINGvalues.yaml:18: resources: {} ships with no defaults. Set sensible production defaults (e.g. 100m/128Mi requests, 500m/512Mi limits) and let users override.

NITChart.yaml:8: appVersion is quoted as "1.0" but should follow SemVer ("1.0.0") for tooling compatibility.

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