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:

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 )

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

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:

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).

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.

Stage 6: Generate Detailed Validation Report (REPORT ONLY)

After completing all validation stages, generate a comprehensive report. This is a REPORT-ONLY stage.

NEVER do any of the following:

• Do NOT use the Edit tool to modify files

• Do NOT use AskUserQuestion to offer to fix issues

• Do NOT prompt the user asking if they want fixes applied

• Do NOT modify any YAML files

ALWAYS do the following:

• Generate a comprehensive validation report

• Show before/after code blocks as SUGGESTIONS only

• Let the user decide what to do after reviewing the report

• End with "Next Steps" for the user to take manually

Summarize all issues found across all stages in a table format:

Categorize by severity:

• Errors (must fix): Syntax errors, missing required fields, dry-run failures

• Warnings (should fix): Style issues, best practice violations

• Info (optional): Suggestions for improvement

Show before/after code blocks for each issue:

For every issue, display explicit before/after YAML snippets showing the suggested fix:

Issue 1: deployment.yaml:21 - Wrong field type (Error)

Current:

        - containerPort: "80"

Suggested Fix:

        - containerPort: 80

Why: containerPort must be an integer, not a string. Kubernetes will reject string values.
Reference: See k8s_best_practices.md "Invalid Values" section.

- 
Provide validation summary:

## Validation Report Summary

File: deployment.yaml
Resources Analyzed: 3 (Deployment, Service, Certificate)

| Stage | Status | Issues Found |
|-------|--------|--------------|
| YAML Syntax | ❌ Failed | 2 errors |
| CRD Detection | ✅ Passed | 1 CRD detected (Certificate) |
| Schema Validation | ❌ Failed | 1 error |
| Dry-Run | ❌ Failed | 1 error |

Total Issues: 4 errors, 2 warnings

## Detailed Findings

[List each issue with before/after code blocks as shown above]

## Next Steps

1. Fix the 4 errors listed above (deployment will fail without these)
2. Consider addressing the 2 warnings for best practices
3. Re-run validation after fixes to confirm resolution

- 
Do NOT modify files - this is a reporting tool only

- Present all findings clearly

- Let the user decide which fixes to apply

- User can request fixes after reviewing the report

Objective Stage Gates (Repeatable)

Use this table to keep stage decisions deterministic:

Stage
Required
Command
Pass/Fail Criteria
Fallback

0 Resource Count
Yes
python3 "$SKILL_DIR/scripts/count_yaml_documents.py" "$TARGET_FILE"

Pass when count output is produced and documents
 is recorded.
Use AWK estimator and mark estimated
.

1 Tool Check
Yes
bash "$SKILL_DIR/scripts/setup_tools.sh"

Pass when command runs and tool availability is known.
Continue with available tools and log skips.

2 YAML Syntax
If yamllint
 available
yamllint -c "$SKILL_DIR/assets/.yamllint" "$TARGET_FILE"

Pass on exit code 0
; fail on lint errors.
Skip with explicit reason if missing binary.

3 CRD Detection
If python3
 available
bash "$SKILL_DIR/scripts/detect_crd_wrapper.sh" "$TARGET_FILE"

Pass when JSON output includes summary
.
Skip CRD extraction and rely on kubeconform clues.

4 Schema
If kubeconform
 available
kubeconform command from Stage 4
Pass when kubeconform reports valid resources.
Skip and record as coverage gap if missing binary.

5 Dry-Run
If kubectl
 available
kubectl apply --dry-run=server -f "$TARGET_FILE"

Pass on successful server dry-run.
Attempt parse-only client fallback with --dry-run=client --validate=false
; if discovery still fails, mark stage skipped.

6 Report
Yes
Report generation
Pass when summary + per-issue snippets + next steps are provided.
No fallback; this stage is mandatory.

Fallback Matrix

Constraint
Action
Report Language

python3
 unavailable
Skip count_yaml_documents.py
 and CRD parser scripts. Use AWK count only.
Python runtime unavailable; CRD parser skipped, resource count is estimated.

yamllint
 unavailable
Skip Stage 2; continue with schema/dry-run stages if available.
YAML lint skipped because yamllint is not installed.

kubeconform
 unavailable
Skip Stage 4; run lint and dry-run only.
Schema validation skipped because kubeconform is not installed.

kubectl
 unavailable
Skip Stage 5 entirely.
Dry-run skipped because kubectl is not installed.

No cluster connectivity
Run server-side first, then attempt parse-only client fallback with --dry-run=client --validate=false
; if it still fails, skip dry-run and continue.
Server-side dry-run unavailable due cluster access; parse-only client-side dry-run attempted (schema checks disabled).

Client dry-run still requires discovery
Treat dry-run as unavailable and rely on lint + schema stages.
Dry-run skipped (cluster discovery unavailable); lint and schema results used.

External docs unavailable
Continue local validation and state documentation gap.
CRD documentation lookup deferred due tooling/network limitation.

Best Practices Reference

For detailed Kubernetes YAML best practices, load the reference:

Read "$SKILL_DIR/references/k8s_best_practices.md"

This reference includes:

- Metadata and label conventions

- Resource limits and requests

- Security context guidelines

- Probe configurations

- Common validation issues and fixes

When to load (ALWAYS load in these cases):

- Schema validation fails with type errors (e.g., string vs integer, invalid values)

- Schema validation reports missing required fields

- kubeconform reports invalid field values or unknown fields

- Dry-run fails with validation errors related to resources, probes, or security

- When explaining why a fix is needed (to provide context from best practices)

Detailed Validation Workflow Reference

For in-depth workflow details and error handling strategies, load the reference:

Read "$SKILL_DIR/references/validation_workflow.md"

This reference includes:

- Detailed command options for each tool

- Error handling strategies

- Multi-resource file handling

- Complete workflow diagram

- Troubleshooting guide

When to load (ALWAYS load in these cases):

- File contains 3 or more resources (multi-document YAML)

- Validation produces errors you haven't seen before or can't immediately diagnose

- Need to understand the complete workflow for debugging

- Errors span multiple validation stages

Working with Multiple Resources

When a YAML file contains multiple resources (separated by ---
):

- Validate the entire file first with yamllint and kubeconform

- If errors occur, identify which resource has issues by checking line numbers

- For dry-run, the file is tested as a unit (Kubernetes processes in order)

- Track issues per-resource when presenting findings to the user

Partial Parsing Behavior

When a multi-document YAML file has some valid and some invalid documents:

Expected behavior:

- The CRD detection script (detect_crd.py
) will parse valid documents and skip invalid ones

- kubeconform will validate resources it can parse and report errors for unparseable ones

- The validation report should clearly show which documents parsed and which failed

Example scenario:
A file with 3 documents where document 1 has a syntax error:

- Document 1 (Deployment): Syntax error at line 8

- Document 2 (Service): Valid

- Document 3 (Certificate CRD): Valid

Expected output:

- CRD detection: Finds Certificate CRD from document 3

- kubeconform: Reports error for document 1, validates documents 2 and 3

- Report: Shows syntax error for document 1, validation results for documents 2 and 3

In your report:

| Document | Resource | Parsing | Validation |
|----------|----------|---------|------------|
| 1 | Deployment | ❌ Syntax error (line 8) | Skipped |
| 2 | Service | ✅ Parsed | ✅ Valid |
| 3 | Certificate | ✅ Parsed | ✅ Valid |

Line Number Reference Style:

- Always use file-absolute line numbers (line numbers relative to the start of the entire file)

- This matches what yamllint, kubeconform, and kubectl report

- Example: If a file has 3 documents and the error is in document 2 which starts at line 35, report as "line 42" (the absolute line in the file), not "line 7" (relative to document start)

- This consistency makes it easy for users to navigate directly to the error in their editor

This ensures users get maximum validation feedback even when some documents have issues.

Error Handling Strategies

Tool Not Available

- Run bash "$SKILL_DIR/scripts/setup_tools.sh"
 to check availability

- Provide installation instructions

- Skip optional stages but document what was skipped

- Continue with available tools

Cluster Access Issues

- Attempt parse-only client-side dry-run with --dry-run=client --validate=false

- Treat this fallback as transport/parsing signal only (--validate=false
 disables schema/type/required-field checks)

- If client dry-run still fails with API discovery/openapi errors, skip dry-run and rely on lint/schema stages

- Document limitations in validation report

CRD Documentation Not Found

- Document that documentation lookup failed

- Attempt validation with kubeconform CRD schemas

- Suggest manual CRD inspection:
kubectl get crd <crd-name>.group -o yaml
kubectl explain <kind>

Validation Stage Failures

- Continue to next stage even if one fails

- Collect all errors before presenting to user

- Prioritize fixing earlier stage errors first

Communication Guidelines

When presenting validation results:

- 
Be clear and concise about what was found

- 
Explain why issues matter (e.g., "This will cause pod creation to fail")

- 
Provide context from best practices when relevant

- 
Group related issues (e.g., all missing label issues together)

- 
Use file:line references for all issues

- 
Show fix complexity - Include a complexity indicator in the issue header:

- [Simple]: Single-line fixes like indentation, typos, or value changes

- [Medium]: Multi-line changes or adding missing fields/sections

- [Complex]: Logic changes, restructuring, or changes affecting multiple resources

Example format in issue header:

**Issue 1: deployment.yaml:8 - Wrong indentation (Error) [Simple]**
**Issue 2: deployment.yaml:15-25 - Missing security context (Warning) [Medium]**
**Issue 3: deployment.yaml - Selector mismatch with Service (Error) [Complex]**

- 
Always provide a comprehensive report including:

- Summary table of all issues by stage

- Before/after code blocks for each issue

- Total count of errors and warnings

- Clear next steps for the user

- 
NEVER offer to apply fixes - this is strictly a reporting tool

- Do not ask "Would you like me to fix this?"

- Do not use AskUserQuestion for fix confirmations

- Present the report and let the user take action

Performance Optimization

Parallel Tool Execution

For improved validation speed, some stages can be executed in parallel:

Can run in parallel (no dependencies):

- yamllint
 (Stage 2) and detect_crd_wrapper.sh
 (Stage 3) can run simultaneously

- Both tools operate independently on the input file

- Results from both are needed before proceeding to schema validation

Example parallel execution:

# Run these in parallel (using & and wait, or parallel tool calls):
yamllint -c "$SKILL_DIR/assets/.yamllint" "$TARGET_FILE"
bash "$SKILL_DIR/scripts/detect_crd_wrapper.sh" "$TARGET_FILE"

Must run sequentially:

- Stage 0 (Resource Count Check) → Before all other stages

- Stage 1 (Tool Check) → Before using any tools

- Stage 4 (Schema Validation) → After CRD detection (needs CRD info for context)

- Stage 5 (Dry-Run) → After schema validation

- Stage 6 (Report) → After all validation stages complete

When to parallelize:

- Files with more than 5 resources benefit most from parallel execution

- For small files (1-2 resources), sequential execution is fine

Version Awareness

Always consider Kubernetes version compatibility:

- Check for deprecated APIs (e.g., extensions/v1beta1
 → apps/v1
)

- For CRDs, ensure the apiVersion matches what's in the cluster

- Use kubectl api-versions
 to list available API versions in the cluster

- Reference version-specific documentation when available

Test Coverage Guidance

The test/
 directory contains example files to exercise all validation paths. Use these to verify skill behavior.

Test Files

Test File
Purpose
Expected Behavior

deployment-test.yaml

Valid standard K8s resource
All stages pass, no errors

certificate-crd-test.yaml

Valid CRD resource
CRD detected, Context7 lookup performed, no errors

comprehensive-test.yaml

Multi-resource with intentional YAML syntax error
Syntax error detected, partial parsing works, CRD found

schema-errors-test.yaml

Valid YAML with intentional schema type errors
yamllint passes; kubeconform fails with 2 JSON-path errors (replicas, containerPort)

Validation Paths to Test

- Happy Path (All Valid)

- File: deployment-test.yaml

- Expected: All stages pass, report shows "0 errors, 0 warnings"

- Commands:

cd "$SKILL_DIR"
python3 scripts/count_yaml_documents.py test/deployment-test.yaml
yamllint -c assets/.yamllint test/deployment-test.yaml
bash scripts/detect_crd_wrapper.sh test/deployment-test.yaml
kubeconform \
  -schema-location default \
  -schema-location 'https://raw.githubusercontent.com/datreeio/CRDs-catalog/main/{{.Group}}/{{.ResourceKind}}_{{.ResourceAPIVersion}}.json' \
  -strict -ignore-missing-schemas -summary -verbose \
  test/deployment-test.yaml
kubectl apply --dry-run=server -f test/deployment-test.yaml

- CRD Detection Path

- File: certificate-crd-test.yaml

- Expected: CRD detected, mcp__context7__resolve-library-id
 and mcp__context7__query-docs
 used

- Commands:

cd "$SKILL_DIR"
python3 scripts/count_yaml_documents.py test/certificate-crd-test.yaml
bash scripts/detect_crd_wrapper.sh test/certificate-crd-test.yaml
kubeconform \
  -schema-location default \
  -schema-location 'https://raw.githubusercontent.com/datreeio/CRDs-catalog/main/{{.Group}}/{{.ResourceKind}}_{{.ResourceAPIVersion}}.json' \
  -strict -ignore-missing-schemas -summary -verbose \
  test/certificate-crd-test.yaml

- Syntax Error Path

- File: comprehensive-test.yaml

- Expected: yamllint catches error, kubeconform reports partial validation, dry-run blocked

- Commands:

cd "$SKILL_DIR"
python3 scripts/count_yaml_documents.py test/comprehensive-test.yaml
yamllint -c assets/.yamllint test/comprehensive-test.yaml
bash scripts/detect_crd_wrapper.sh test/comprehensive-test.yaml
kubeconform \
  -schema-location default \
  -schema-location 'https://raw.githubusercontent.com/datreeio/CRDs-catalog/main/{{.Group}}/{{.ResourceKind}}_{{.ResourceAPIVersion}}.json' \
  -strict -ignore-missing-schemas -summary -verbose \
  test/comprehensive-test.yaml
kubectl apply --dry-run=server -f test/comprehensive-test.yaml

- Multi-Resource Partial Parsing

- File: comprehensive-test.yaml
 (has 3 resources, 1 with syntax error)

- Expected: 2/3 resources validated, parse error reported for document 1

- Commands:

cd "$SKILL_DIR"
python3 scripts/count_yaml_documents.py test/comprehensive-test.yaml
bash scripts/detect_crd_wrapper.sh test/comprehensive-test.yaml

- Schema Validation Error Path (type mismatches)

- File: schema-errors-test.yaml

- Expected: yamllint passes (valid YAML), kubeconform fails with 2 JSON-path schema errors

- Note: kubeconform reports JSON paths, not line numbers — locate fields manually in the YAML

- Commands:

cd "$SKILL_DIR"
python3 scripts/count_yaml_documents.py test/schema-errors-test.yaml
yamllint -c assets/.yamllint test/schema-errors-test.yaml
bash scripts/detect_crd_wrapper.sh test/schema-errors-test.yaml
kubeconform \
  -schema-location default \
  -schema-location 'https://raw.githubusercontent.com/datreeio/CRDs-catalog/main/{{.Group}}/{{.ResourceKind}}_{{.ResourceAPIVersion}}.json' \
  -strict -ignore-missing-schemas -summary -verbose \
  test/schema-errors-test.yaml

- No Cluster Access Path

- Any valid file with no kubectl cluster configured

- Expected: Server-side dry-run fails; parse-only client-side fallback is attempted (no schema guarantees) and may still fail if API discovery is unavailable

- Commands:

cd "$SKILL_DIR"
KUBECONFIG=/tmp/nonexistent-kubeconfig kubectl apply --dry-run=server -f test/deployment-test.yaml
KUBECONFIG=/tmp/nonexistent-kubeconfig kubectl apply --dry-run=client --validate=false -f test/deployment-test.yaml

- Missing Tools Path

- Test by temporarily removing a tool from PATH

- Expected: setup_tools.sh reports missing tools and prints install instructions, validation continues with available tools

- Commands:

cd "$SKILL_DIR"
PATH="/usr/bin:/bin" bash scripts/setup_tools.sh

Creating New Test Files

When adding test files:

- Name files descriptively: <scenario>-test.yaml

- Document expected behavior in comments at top of file

- Include intentional errors for error-path tests

- Test both standard K8s resources and CRDs

Expected Report Structure

For any validation, the report should include:

-  Summary table with issue counts by severity

-  Stage-by-stage status table (passed/failed/skipped)

-  Document parsing table (for multi-resource files)

-  Before/after code blocks for each issue

-  Fix complexity indicators ([Simple], [Medium], [Complex])

-  File-absolute line numbers

-  "Next Steps" section

Done Criteria

Validation is complete only when all conditions are true:

- Stage gates were evaluated in order and every skipped stage includes a reason.

- Resource count came from count_yaml_documents.py
 (or documented AWK fallback).

- CRD lookups used mcp__context7__resolve-library-id
 + mcp__context7__query-docs
, with web.search_query
 fallback only when needed.

- Report-only boundary was preserved (no edits, no fix-application prompts).

- Output includes exact commands run, findings by severity, and manual next steps.

Resources

scripts/

detect_crd_wrapper.sh

- Wrapper script that handles Python dependency management

- Automatically creates temporary venv if PyYAML is not available

- Calls detect_crd.py to parse YAML files

- Usage: bash "$SKILL_DIR/scripts/detect_crd_wrapper.sh" "$TARGET_FILE"

detect_crd.py

- Parses YAML files to identify Custom Resource Definitions

- Extracts kind, apiVersion, group, and version information

- Outputs JSON for programmatic processing

- Requires PyYAML (handled automatically by wrapper script)

- Can be called directly: python3 "$SKILL_DIR/scripts/detect_crd.py" "$TARGET_FILE"

count_yaml_documents.py

- Deterministically counts non-empty YAML documents in a multi-doc file

- Returns JSON with document count and separators

- Use before Stage 1 to decide whether to load deep workflow reference

- Usage: python3 "$SKILL_DIR/scripts/count_yaml_documents.py" "$TARGET_FILE"

setup_tools.sh

- Checks for required validation tools

- Provides installation instructions for missing tools

- Verifies versions of installed tools

- Usage: bash "$SKILL_DIR/scripts/setup_tools.sh"

references/

k8s_best_practices.md

- Comprehensive guide to Kubernetes YAML best practices

- Covers metadata, labels, resource limits, security context

- Common validation issues and how to fix them

- Load when providing context for validation errors

validation_workflow.md

- Detailed validation workflow with all stages

- Command options and configurations

- Error handling strategies

- Complete workflow diagram

- Load for complex validation scenarios

assets/

.yamllint

- Pre-configured yamllint rules for Kubernetes YAML

- Follows Kubernetes conventions (2-space indentation, line length, etc.)

- Can be customized per project

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