akin-ozer/cc-devops-skills/k8s-yaml-validator

Kubernetes YAML Validator

Overview

This skill provides a comprehensive validation workflow for Kubernetes YAML resources, combining syntax linting, schema validation, cluster dry-run testing, and intelligent CRD documentation lookup. Validate any Kubernetes manifest with confidence before applying it to the cluster.

IMPORTANT: This is a REPORT-ONLY validation tool. Do NOT modify files, do NOT use Edit tool, do NOT use AskUserQuestion to offer fixes. Generate a comprehensive validation report with suggested fixes shown as before/after code blocks, then let the user decide what to do next.

Trigger Phrases

Use this skill when prompts look like:

• "Validate this Kubernetes YAML before deploy."
• "Lint these manifests and report what is broken."
• "Check this CRD manifest and explain schema issues."
• "Run dry-run checks on this manifest."
• "Find line-level errors in this multi-document YAML."

When to Use This Skill

Invoke this skill when:

• Validating Kubernetes YAML files before applying to a cluster
• Debugging YAML syntax or formatting errors
• Working with Custom Resource Definitions (CRDs) and need documentation
• Performing dry-run tests to catch admission controller errors
• Ensuring YAML follows Kubernetes best practices
• Understanding what validation errors exist in manifests (report-only, user fixes manually)
• The user asks to "validate", "lint", "check", or "test" Kubernetes YAML files

Read-Only Boundary (Mandatory)

This skill is strictly report-only:

• Do NOT modify any user files.
• Do NOT run Edit for fixes.
• Do NOT ask for permission to apply fixes.
• Do provide before/after snippets as suggestions in the report.

Deterministic Path Setup

Run with explicit paths so commands are repeatable:

REPO_ROOT="$(git rev-parse --show-toplevel 2>/dev/null)"
SKILL_DIR="$REPO_ROOT/devops-skills-plugin/skills/k8s-yaml-validator"
TARGET_FILE="$REPO_ROOT/<relative/path/to/file.yaml>"

Path checks:

• If REPO_ROOT is empty, stop and ask for repository root.
• If SKILL_DIR does not exist, stop and report path mismatch.
• If TARGET_FILE does not exist, stop and ask for the correct file.

Validation Workflow

Follow this sequential validation workflow. Each stage catches different types of issues:

Stage 0: Pre-Validation Setup (Deterministic Resource Count)

Before running validators, count documents using the bundled script:

python3 "$SKILL_DIR/scripts/count_yaml_documents.py" "$TARGET_FILE"

Expected output (example):

{
  "file": ".../manifests.yaml",
  "documents": 3,
  "separators": 2
}

Gate rules:

• If documents >= 3, load references/validation_workflow.md before Stage 1.
• Always include the document count in the final report summary.
• If python3 is unavailable, use fallback:

awk 'BEGIN{d=0;seen=0} /^[[:space:]]*---[[:space:]]*$/ {if(seen){d++;seen=0}; next} /^[[:space:]]*#/ {next} NF{seen=1} END{if(seen)d++; print d}' "$TARGET_FILE"

and mark the count as estimated in the report.

Stage 1: Tool Check

Before starting validation, verify required tools are installed:

bash "$SKILL_DIR/scripts/setup_tools.sh"

Required tools:

• yamllint: YAML syntax and style linting
• kubeconform: Kubernetes schema validation with CRD support
• kubectl: Cluster dry-run testing (optional but recommended)

If tools are missing, display installation guidance from script output and continue with available tools. Document missing tools and skipped stages in the report.

Stage 2: YAML Syntax Validation

Validate YAML syntax and formatting using yamllint:

yamllint -c "$SKILL_DIR/assets/.yamllint" "$TARGET_FILE"

Common issues caught:

• Indentation errors (tabs vs spaces)
• Trailing whitespace
• Line length violations
• Syntax errors
• Duplicate keys

Reporting approach:

• Report all syntax issues with file:line references
• For fixable issues, show suggested before/after code blocks
• Continue to next validation stage to collect all issues before reporting

Stage 3: CRD Detection and Documentation Lookup

Before schema validation, detect if the YAML contains Custom Resource Definitions:

bash "$SKILL_DIR/scripts/detect_crd_wrapper.sh" "$TARGET_FILE"

The wrapper script automatically handles Python dependencies by creating a temporary virtual environment if PyYAML is not available.

Resilient Parsing: The script is resilient to syntax errors in individual documents. If a multi-document YAML file has some valid and some invalid documents, the script will:

• Parse valid documents and detect their CRDs
• Report errors for invalid documents but continue processing
• This matches kubeconform's behavior of validating 2/3 resources even when 1/3 has syntax errors

The script outputs JSON with resource information and parse status:

{
  "resources": [
    {
      "kind": "Certificate",
      "apiVersion": "cert-manager.io/v1",
      "group": "cert-manager.io",
      "version": "v1",
      "isCRD": true,
      "name": "example-cert"
    }
  ],
  "parseErrors": [
    {
      "document": 1,
      "start_line": 2,
      "error_line": 6,
      "error": "mapping values are not allowed in this context"
    }
  ],
  "summary": {
    "totalDocuments": 3,
    "parsedSuccessfully": 2,
    "parseErrors": 1,
    "crdsDetected": 1
  }
}

For each detected CRD:

1. Try Context7 MCP first (preferred):

• Resolve library:
• Tool: mcp__context7__resolve-library-id
• libraryName: CRD project name (example: cert-manager for cert-manager.io)
• Query docs:
• Tool: mcp__context7__query-docs
• libraryId: resolved library ID from previous step
• query: include CRD kind, group, and version (example: Certificate cert-manager.io v1 required fields in spec)

1. Fallback to `web.search_query` if Context7 fails or returns insufficient details:

``` Search query pattern: "<kind>" "<group>" kubernetes CRD "<version>" documentation spec

Example: "Certificate" "cert-manager.io" kubernetes CRD "v1" documentation spec ```

1. Extract key information:

• Required fields in spec
• Field types and validation rules
• Examples from documentation
• Version-specific changes or deprecations

Secondary CRD Detection via kubeconform: If detect_crd_wrapper.sh cannot identify CRDs (for example, syntax errors in all documents), but kubeconform still validates a CRD resource, look up docs for that CRD anyway. Parse kubeconform output to identify validated CRDs and perform Context7/web.search_query lookups.

Why this matters: CRDs have custom schemas not available in standard Kubernetes validation tools. Understanding the CRD's spec requirements prevents validation errors and ensures correct resource configuration.

Stage 4: Schema Validation

Validate against Kubernetes schemas using kubeconform:

kubeconform \
  -schema-location default \
  -schema-location 'https://raw.githubusercontent.com/datreeio/CRDs-catalog/main/{{.Group}}/{{.ResourceKind}}_{{.ResourceAPIVersion}}.json' \
  -strict \
  -ignore-missing-schemas \
  -summary \
  -verbose \
  "$TARGET_FILE"

Options explained:

• -strict: Reject unknown fields (recommended for production - catches typos)
• -ignore-missing-schemas: Skip validation for CRDs without available schemas
• -kubernetes-version 1.30.0: Validate against specific K8s version

Common issues caught:

• Invalid apiVersion or kind
• Missing required fields
• Wrong field types
• Invalid enum values
• Unknown fields (with -strict)

For CRDs: If kubeconform reports "no schema found", this is expected. Use the documentation from Stage 3 to manually validate the spec fields.

kubeconform line number behavior — two distinct cases:

kubeconform does NOT report file-absolute line numbers. You must translate:

1. Parse errors (e.g. error converting YAML to JSON: yaml: line N):

• N is document-relative (line N within that document's content).
• Convert to file-absolute: file_line = doc_start_line + N - 1
• doc_start_line comes from the start_line field in detect_crd_wrapper.sh output.
• Example: document starts at file line 4, kubeconform says yaml: line 5 →

file-absolute line = 4 + 5 − 1 = line 8 (matches yamllint output).

1. Schema validation errors (e.g. got string, want integer):

• kubeconform reports JSON path only, no line number.
• Example: at '/spec/template/spec/containers/0/ports/0/containerPort': got string, want integer
• To find the line: search the YAML file for the field name (e.g. containerPort) within

the relevant document section, using file-absolute line numbers from the surrounding context.

Always present line numbers as file-absolute in the validation report even when translating from kubeconform's document-relative output.

Stage 5: Cluster Dry-Run (if available)

IMPORTANT: Always try server-side dry-run first. Server-side validation catches more issues than client-side because it runs through admission controllers and webhooks.

Decision Tree:

1. Try server-side dry-run first:
   kubectl apply --dry-run=server -f "$TARGET_FILE"
 
   └─ If SUCCESS → Use results, continue to Stage 6
 
   └─ If FAILS with connection error (e.g., "connection refused",
      "unable to connect", "no configuration"):
      │
      ├─ 2. Attempt client-side dry-run (parse-only fallback):
      │     kubectl apply --dry-run=client --validate=false -f "$TARGET_FILE"
      │
      │     ├─ If SUCCESS:
      │     │    Document in report: "Server-side validation skipped (no cluster access); client fallback ran in parse-only mode"
      │     │
      │     └─ If FAILS with discovery/openapi error (e.g., "unable to recognize",
      │        "failed to download openapi", "couldn't get current server API group list"):
      │        Document in report: "Dry-run skipped (cluster discovery unavailable)"
      │        Continue to Stage 6
      │
      └─ If FAILS with validation error (e.g., "admission webhook denied",
         "resource quota exceeded", "invalid value"):
         └─ Record the error, continue to Stage 6
 
   └─ If FAILS with parse error (e.g., "error converting YAML to JSON",
      "yaml: line X: mapping values are not allowed"):
      └─ Record the error, skip client-side dry-run (same error will occur)
         Document in report: "Dry-run blocked by YAML syntax errors - fix syntax first"
         Continue to Stage 6

Note: Parse errors from earlier stages (yamllint, kubeconform) will also cause dry-run to fail. Do NOT attempt client-side dry-run as a fallback for parse errors - it will produce the same error. Parse errors must be fixed before dry-run validation can proceed.

Server-side dry-run catches:

• Admission controller rejections
• Policy violations (PSP, OPA, Kyverno, etc.)
• Resource quota violations
• Missing namespaces
• Invalid ConfigMap/Secret references
• Webhook validations

Client-side dry-run with `--validate=false` catches (fallback, when command succeeds):

• YAML/JSON conversion and request-construction issues
• Whether kubectl can process and submit the manifest shape in client mode
• Note: --validate=false disables schema/type/required-field validation and still does NOT catch admission controller or policy issues.

Document in your report which mode was used:

• If server-side: "Full cluster validation performed"
• If client-side with --validate=false: "Limited parse-only validation (no cluster access) - schema and admission policies not checked"
• If skipped: "Dry-run skipped - kubectl not available"
• If skipped after client fallback attempt: "Dry-run skipped (cluster discovery unavailable)"

For updates to existing resources:

kubectl diff -f "$TARGET_FILE"

This shows what would change, helping catch unintended modifications.