N8n Validation Expert V2

n8n Validation Expert

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/n8n-validation-expert from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

n8n Validation Expert Expert guide for interpreting and fixing n8n validation errors.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Validation Philosophy, Error Severity Levels, The Validation Loop, Validation Profiles, Common Error Types, Auto-Sanitization System.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• You need to interpret or fix validation errors in an n8n workflow.
• The task involves missingrequired, invalidvalue, expression failures, or iterative validate-fix loops.
• You want concrete remediation guidance for workflow validation output.
• Use when the request clearly matches the imported source intent: Expert guide for interpreting and fixing n8n validation errors.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Node configurations - Each node valid
2. Connections - No broken references
3. Expressions - Syntax and references valid
4. Flow - Logical workflow structure
5. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
6. Read the overview and provenance files before loading any copied upstream support files.
7. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.

Imported Workflow Notes

Imported: Workflow Validation

validate_workflow (Structure)

Validates entire workflow, not just individual nodes

Checks:

1. Node configurations - Each node valid
2. Connections - No broken references
3. Expressions - Syntax and references valid
4. Flow - Logical workflow structure

Example:

validate_workflow({
  workflow: {
    nodes: [...],
    connections: {...}
  },
  options: {
    validateNodes: true,
    validateConnections: true,
    validateExpressions: true,
    profile: "runtime"
  }
})

Common Workflow Errors

1. Broken Connections

{
  "error": "Connection from 'Transform' to 'NonExistent' - target node not found"
}

Fix: Remove stale connection or create missing node

2. Circular Dependencies

{
  "error": "Circular dependency detected: Node A → Node B → Node A"
}

Fix: Restructure workflow to remove loop

3. Multiple Start Nodes

{
  "warning": "Multiple trigger nodes found - only one will execute"
}

Fix: Remove extra triggers or split into separate workflows

4. Disconnected Nodes

{
  "warning": "Node 'Transform' is not connected to workflow flow"
}

Fix: Connect node or remove if unused

Imported: Summary

Key Points:

1. Validation is iterative (avg 2-3 cycles, 23s + 58s)
2. Errors must be fixed, warnings are optional
3. Auto-sanitization fixes operator structures automatically
4. Use runtime profile for balanced validation
5. False positives exist - learn to recognize them
6. Read error messages - they contain fix guidance

Validation Process:

1. Validate → Read errors → Fix → Validate again
2. Repeat until valid (usually 2-3 iterations)
3. Review warnings and decide if acceptable
4. Deploy with confidence

Related Skills:

• n8n MCP Tools Expert - Use validation tools correctly
• n8n Expression Syntax - Fix expression errors
• n8n Node Configuration - Understand required fields

Imported: Validation Philosophy

Validate early, validate often

Validation is typically iterative:

• Expect validation feedback loops
• Usually 2-3 validate → fix cycles
• Average: 23s thinking about errors, 58s fixing them

Key insight: Validation is an iterative process, not one-shot!

Examples

Example 1: Ask for the upstream workflow directly

Use @n8n-validation-expert-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @n8n-validation-expert-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @n8n-validation-expert-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @n8n-validation-expert-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Validate after every significant change
• Read error messages completely
• Fix errors iteratively (one at a time)
• Use runtime profile for pre-deployment
• Check valid field before assuming success
• Trust auto-sanitization for operator issues
• Use get_node when unclear about requirements

Imported Operating Notes

Imported: Best Practices

✅ Do

• Validate after every significant change
• Read error messages completely
• Fix errors iteratively (one at a time)
• Use runtime profile for pre-deployment
• Check valid field before assuming success
• Trust auto-sanitization for operator issues
• Use get_node when unclear about requirements
• Document false positives you accept

❌ Don't

• Skip validation before activation
• Try to fix all errors at once
• Ignore error messages
• Use strict profile during development (too noisy)
• Assume validation passed (always check result)
• Manually fix auto-sanitization issues
• Deploy with unresolved errors
• Ignore all warnings (some are important!)

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/n8n-validation-expert, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Error Severity Levels

1. Errors (Must Fix)

Blocks workflow execution - Must be resolved before activation

Types:

• missing_required - Required field not provided
• invalid_value - Value doesn't match allowed options
• type_mismatch - Wrong data type (string instead of number)
• invalid_reference - Referenced node doesn't exist
• invalid_expression - Expression syntax error

Example:

{
  "type": "missing_required",
  "property": "channel",
  "message": "Channel name is required",
  "fix": "Provide a channel name (lowercase, no spaces, 1-80 characters)"
}

2. Warnings (Should Fix)

Doesn't block execution - Workflow can be activated but may have issues

Types:

• best_practice - Recommended but not required
• deprecated - Using old API/feature
• performance - Potential performance issue

Example:

{
  "type": "best_practice",
  "property": "errorHandling",
  "message": "Slack API can have rate limits",
  "suggestion": "Add onError: 'continueRegularOutput' with retryOnFail"
}

3. Suggestions (Optional)

Nice to have - Improvements that could enhance workflow

Types:

• optimization - Could be more efficient
• alternative - Better way to achieve same result

Imported: The Validation Loop

Pattern from Telemetry

7,841 occurrences of this pattern:

1. Configure node
   ↓
2. validate_node (23 seconds thinking about errors)
   ↓
3. Read error messages carefully
   ↓
4. Fix errors
   ↓
5. validate_node again (58 seconds fixing)
   ↓
6. Repeat until valid (usually 2-3 iterations)

Example

// Iteration 1
let config = {
  resource: "channel",
  operation: "create"
};

const result1 = validate_node({
  nodeType: "nodes-base.slack",
  config,
  profile: "runtime"
});
// → Error: Missing "name"

// ⏱️  23 seconds thinking...

// Iteration 2
config.name = "general";

const result2 = validate_node({
  nodeType: "nodes-base.slack",
  config,
  profile: "runtime"
});
// → Error: Missing "text"

// ⏱️  58 seconds fixing...

// Iteration 3
config.text = "Hello!";

const result3 = validate_node({
  nodeType: "nodes-base.slack",
  config,
  profile: "runtime"
});
// → Valid! ✅

This is normal! Don't be discouraged by multiple iterations.

Imported: Validation Profiles

Choose the right profile for your stage:

minimal

Use when: Quick checks during editing

Validates:

• Only required fields
• Basic structure

Pros: Fastest, most permissive Cons: May miss issues

runtime (RECOMMENDED)

Use when: Pre-deployment validation

Validates:

• Required fields
• Value types
• Allowed values
• Basic dependencies

Pros: Balanced, catches real errors Cons: Some edge cases missed

This is the recommended profile for most use cases

ai-friendly

Use when: AI-generated configurations

Validates:

• Same as runtime
• Reduces false positives
• More tolerant of minor issues

Pros: Less noisy for AI workflows Cons: May allow some questionable configs

strict

Use when: Production deployment, critical workflows

Validates:

• Everything
• Best practices
• Performance concerns
• Security issues

Pros: Maximum safety Cons: Many warnings, some false positives

Imported: Common Error Types

1. missing_required

What it means: A required field is not provided

How to fix:

1. Use get_node to see required fields
2. Add the missing field to your configuration
3. Provide an appropriate value

Example:

// Error
{
  "type": "missing_required",
  "property": "channel",
  "message": "Channel name is required"
}

// Fix
config.channel = "#general";

2. invalid_value

What it means: Value doesn't match allowed options

How to fix:

1. Check error message for allowed values
2. Use get_node to see options
3. Update to a valid value

Example:

// Error
{
  "type": "invalid_value",
  "property": "operation",
  "message": "Operation must be one of: post, update, delete",
  "current": "send"
}

// Fix
config.operation = "post";  // Use valid operation

3. type_mismatch

What it means: Wrong data type for field

How to fix:

1. Check expected type in error message
2. Convert value to correct type

Example:

// Error
{
  "type": "type_mismatch",
  "property": "limit",
  "message": "Expected number, got string",
  "current": "100"
}

// Fix
config.limit = 100;  // Number, not string

4. invalid_expression

What it means: Expression syntax error

How to fix:

1. Use n8n Expression Syntax skill
2. Check for missing {{}} or typos
3. Verify node/field references

Example:

// Error
{
  "type": "invalid_expression",
  "property": "text",
  "message": "Invalid expression: $json.name",
  "current": "$json.name"
}

// Fix
config.text = "={{$json.name}}";  // Add {{}}

5. invalid_reference

What it means: Referenced node doesn't exist

How to fix:

1. Check node name spelling
2. Verify node exists in workflow
3. Update reference to correct name

Example:

// Error
{
  "type": "invalid_reference",
  "property": "expression",
  "message": "Node 'HTTP Requets' does not exist",
  "current": "={{$node['HTTP Requets'].json.data}}"
}

// Fix - correct typo
config.expression = "={{$node['HTTP Request'].json.data}}";

Imported: Auto-Sanitization System

What It Does

Automatically fixes common operator structure issues on ANY workflow update

Runs when:

• n8n_create_workflow
• n8n_update_partial_workflow
• Any workflow save operation

What It Fixes

1. Binary Operators (Two Values)

Operators: equals, notEquals, contains, notContains, greaterThan, lessThan, startsWith, endsWith

Fix: Removes singleValue property (binary operators compare two values)

Before:

{
  "type": "boolean",
  "operation": "equals",
  "singleValue": true  // ❌ Wrong!
}

After (automatic):

{
  "type": "boolean",
  "operation": "equals"
  // singleValue removed ✅
}

2. Unary Operators (One Value)

Operators: isEmpty, isNotEmpty, true, false

Fix: Adds singleValue: true (unary operators check single value)

Before:

{
  "type": "boolean",
  "operation": "isEmpty"
  // Missing singleValue ❌
}

After (automatic):

{
  "type": "boolean",
  "operation": "isEmpty",
  "singleValue": true  // ✅ Added
}

3. IF/Switch Metadata

Fix: Adds complete conditions.options metadata for IF v2.2+ and Switch v3.2+

What It CANNOT Fix

1. Broken Connections

References to non-existent nodes

Solution: Use cleanStaleConnections operation in n8n_update_partial_workflow

2. Branch Count Mismatches

3 Switch rules but only 2 output connections

Solution: Add missing connections or remove extra rules

3. Paradoxical Corrupt States

API returns corrupt data but rejects updates

Solution: May require manual database intervention

Imported: False Positives

What Are They?

Validation warnings that are technically "wrong" but acceptable in your use case

Common False Positives

1. "Missing error handling"

Warning: No error handling configured

When acceptable:

• Simple workflows where failures are obvious
• Testing/development workflows
• Non-critical notifications

When to fix: Production workflows handling important data

2. "No retry logic"

Warning: Node doesn't retry on failure

When acceptable:

• APIs with their own retry logic
• Idempotent operations
• Manual trigger workflows

When to fix: Flaky external services, production automation

3. "Missing rate limiting"

Warning: No rate limiting for API calls

When acceptable:

• Internal APIs with no limits
• Low-volume workflows
• APIs with server-side rate limiting

When to fix: Public APIs, high-volume workflows

4. "Unbounded query"

Warning: SELECT without LIMIT

When acceptable:

• Small known datasets
• Aggregation queries
• Development/testing

When to fix: Production queries on large tables

Reducing False Positives

Use ai-friendly profile:

validate_node({
  nodeType: "nodes-base.slack",
  config: {...},
  profile: "ai-friendly"  // Fewer false positives
})

Imported: Validation Result Structure

Complete Response

{
  "valid": false,
  "errors": [
    {
      "type": "missing_required",
      "property": "channel",
      "message": "Channel name is required",
      "fix": "Provide a channel name (lowercase, no spaces)"
    }
  ],
  "warnings": [
    {
      "type": "best_practice",
      "property": "errorHandling",
      "message": "Slack API can have rate limits",
      "suggestion": "Add onError: 'continueRegularOutput'"
    }
  ],
  "suggestions": [
    {
      "type": "optimization",
      "message": "Consider using batch operations for multiple messages"
    }
  ],
  "summary": {
    "hasErrors": true,
    "errorCount": 1,
    "warningCount": 1,
    "suggestionCount": 1
  }
}

How to Read It

1. Check valid field

if (result.valid) {
  // ✅ Configuration is valid
} else {
  // ❌ Has errors - must fix before deployment
}

2. Fix errors first

result.errors.forEach(error => {
  console.log(`Error in ${error.property}: ${error.message}`);
  console.log(`Fix: ${error.fix}`);
});

3. Review warnings

result.warnings.forEach(warning => {
  console.log(`Warning: ${warning.message}`);
  console.log(`Suggestion: ${warning.suggestion}`);
  // Decide if you need to address this
});

4. Consider suggestions

// Optional improvements
// Not required but may enhance workflow

Imported: Recovery Strategies

Strategy 1: Start Fresh

When: Configuration is severely broken

Steps:

1. Note required fields from get_node
2. Create minimal valid configuration
3. Add features incrementally
4. Validate after each addition

Strategy 2: Binary Search

When: Workflow validates but executes incorrectly

Steps:

1. Remove half the nodes
2. Validate and test
3. If works: problem is in removed nodes
4. If fails: problem is in remaining nodes
5. Repeat until problem isolated

Strategy 3: Clean Stale Connections

When: "Node not found" errors

Steps:

n8n_update_partial_workflow({
  id: "workflow-id",
  operations: [{
    type: "cleanStaleConnections"
  }]
})

Strategy 4: Use Auto-fix

When: Operator structure errors

Steps:

n8n_autofix_workflow({
  id: "workflow-id",
  applyFixes: false  // Preview first
})

// Review fixes, then apply
n8n_autofix_workflow({
  id: "workflow-id",
  applyFixes: true
})

Imported: Detailed Guides

For comprehensive error catalogs and false positive examples:

• ERROR_CATALOG.md - Complete list of error types with examples
• FALSE_POSITIVES.md - When warnings are acceptable

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.