N8n Validation Expert

N8N Validation Expert

Orchestrates intelligent skill selection and execution for n8n validation expert workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def validate_n8n_workflow_structure(
    workflow_json: Dict,
    strict_mode: bool = True
) -> Dict:
    """Validate n8n workflow JSON structure and node configurations.
    
    Applies the 5 Laws of Elegant Defense to n8n validation:
    - Early exit on malformed JSON or missing required fields
    - Parse inputs at boundary (workflow JSON) before processing
    - Return new validation report, never mutate original workflow
    - Fail fast on invalid node types or missing credential references
    
    Args:
        workflow_json: Parsed n8n workflow dictionary
        strict_mode: If True, fail on missing optional credentials; if False, warn only
        
    Returns:
        Validation report with status, errors, warnings, and node analysis
    """
    # Guard clause - Early Exit (Law 1)
    if not isinstance(workflow_json, dict):
        raise ValueError("Workflow must be a valid JSON object")
    if "nodes" not in workflow_json or not isinstance(workflow_json["nodes"], list):
        raise ValueError("Workflow must contain a 'nodes' array")
        
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    valid_node_types = {"n8n-nodes-base.httpRequest", "n8n-nodes-base.webhook", 
                        "n8n-nodes-base.if", "n8n-nodes-base.code", "n8n-nodes-base.set"}
    validation_report = {
        "status": "valid",
        "errors": [],
        "warnings": [],
        "nodes_analyzed": 0,
        "credential_references": []
    }
    
    for node in workflow_json["nodes"]:
        node_type = node.get("type", "")
        validation_report["nodes_analyzed"] += 1
        
        if node_type not in valid_node_types:
            if strict_mode:
                validation_report["errors"].append(f"Invalid node type: {node_type}")
                validation_report["status"] = "invalid"
            else:
                validation_report["warnings"].append(f"Unknown node type: {node_type}")
                
        # Check credential references
        if "credentials" in node:
            for cred_name, cred_type in node["credentials"].items():
                validation_report["credential_references"].append({
                    "node": node.get("name"),
                    "type": cred_type,
                    "required": True
                })
                
    # Atomic Predictability (Law 3) - Return new dict, don't mutate workflow
    return validation_report

Pattern 2: Execution with Fallback

def execute_n8n_validation_with_fallback(
    workflow_json: Dict,
    credential_store: Dict,
    max_validation_attempts: int = 2
) -> Dict:
    """Execute n8n workflow validation with fallback chain for resilience.
    
    Implements the Fail Fast, Fail Loud principle (Law 4):
    - Invalid node configurations halt immediately with descriptive errors
    - No silent failures or partial validation results
    
    Fallback chain:
    1. Retry validation with relaxed schema checks
    2. Skip problematic nodes and validate remaining workflow
    3. Defer to human operator if critical execution paths are broken
    
    Args:
        workflow_json: Parsed n8n workflow dictionary
        credential_store: Dictionary of available credential configurations
        max_validation_attempts: Maximum retry attempts before fallback
        
    Returns:
        Validation execution result with metadata (success, timing, confidence)
    """
    # Guard clause - validate credential store (Early Exit)
    if not isinstance(credential_store, dict):
        raise ValueError("Credential store must be a dictionary")
        
    # Parse context - Ensure trusted state (Law 2)
    validated_workflow = _normalize_n8n_schema(workflow_json)
    
    for attempt in range(max_validation_attempts + 1):
        try:
            report = validate_n8n_workflow_structure(validated_workflow, strict_mode=(attempt == 0))
            
            # Success - Atomic Predictability (Law 3)
            return {
                "success": True,
                "validation_report": report,
                "attempts": attempt + 1,
                "latency_ms": _calculate_latency(),
                "confidence": 0.95 if report["status"] == "valid" else 0.6
            }
            
        except InvalidNodeConfigError as e:
            # Fail Fast - Don't try to patch bad data (Law 4)
            raise ValueError(f"Invalid node configuration: {str(e)}") from e
            
        except MissingCredentialError as e:
            # Transient error - try fallback (skip node or use default)
            if attempt == max_validation_attempts:
                return _apply_n8n_validation_fallback(validated_workflow, credential_store)
                
    # All retries exhausted - Fail Loud (Law 4)
    raise ValueError("Workflow validation failed after all fallback attempts")

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Related Skills

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