paulpas N8N Expression Syntax
Orchestrates intelligent skill selection and execution for n8n expression syntax 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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 parse_n8n_expression(
expression: str,
available_context: Dict[str, Any],
strict_mode: bool = True
) -> Dict[str, Any]:
"""Parse and validate an n8n expression string.
Handles n8n-specific syntax: {{ $json.field }}, {{ $node['NodeName'].json.field }},
{{ $now }}, {{ $item('data', 0).id }}, and arithmetic/logic operations.
Args:
expression: Raw n8n expression string (e.g., "{{ $json.status == 'active' }}")
available_context: Current workflow context (nodes, items, parameters)
strict_mode: If True, raises on undefined paths; if False, returns None for missing
Returns:
Parsed AST-like dict with tokens, resolved values, and validation status
Raises:
ValueError: If expression is empty or malformed
"""
# Guard clause - Early Exit (Law 1)
if not expression or not expression.strip():
raise ValueError("Expression cannot be empty")
# Extract expression body from {{ }} delimiters
match = re.match(r'^\{\{\s*(.+?)\s*\}\}$', expression.strip())
if not match:
raise ValueError("Invalid n8n expression format: missing {{ }} delimiters")
inner_expr = match.group(1)
# Parse input - Make Illegal States Unrepresentable (Law 2)
tokens = _tokenize_n8n_expression(inner_expr)
resolved = _resolve_n8n_tokens(tokens, available_context, strict_mode)
# Atomic Predictability (Law 3) - Return new dict, don't mutate context
result = {
"original": expression,
"parsed_tokens": tokens,
"resolved_value": resolved,
"is_valid": True,
"strict_mode": strict_mode,
"timestamp": time.time()
}
return result
Pattern 2: Execution with Fallback
def execute_n8n_expression(
parsed_expr: Dict[str, Any],
workflow_context: Dict[str, Any],
fallback_strategy: str = "default"
) -> Dict[str, Any]:
"""Execute a validated n8n expression with fallback handling.
Implements the Fail Fast, Fail Loud principle (Law 4):
- Invalid node references halt immediately with descriptive errors
- Missing fields return None or configured default instead of crashing
Fallback chain:
1. Retry with expanded context (if node data is still loading)
2. Try alternative expression path (if primary path fails)
3. Defer to static value or error string (for critical workflows)
Args:
parsed_expr: Output from parse_n8n_expression
workflow_context: Full workflow state including all node outputs
fallback_strategy: 'default', 'retry', 'static', or 'error'
Returns:
Execution result with metadata (success, timing, resolved_value)
Raises:
ValueError: If all retries and fallbacks exhausted
"""
# Guard clause - validate parsed expression (Early Exit)
if not parsed_expr.get("is_valid"):
raise ValueError(f"Cannot execute invalid expression: {parsed_expr.get('original')}")
# Parse context - Ensure trusted state (Law 2)
validated_context = _normalize_workflow_context(workflow_context)
attempts = 0
max_attempts = 2 if fallback_strategy == "retry" else 1
for attempt in range(max_attempts):
try:
result = _evaluate_n8n_ast(parsed_expr["parsed_tokens"], validated_context)
# Success - Atomic Predictability (Law 3)
return {
"success": True,
"expression": parsed_expr["original"],
"resolved_value": result,
"attempts": attempt + 1,
"latency_ms": _calculate_latency(),
"fallback_used": False
}
except UndefinedPathError as e:
# Fail Fast - Don't try to patch bad data (Law 4)
if fallback_strategy == "static":
return {"success": True, "resolved_value": None, "fallback_used": True}
raise ValueError(f"Undefined path in expression: {str(e)}") from e
except ContextLoadingError as e:
# Transient error - try fallback
if attempt == max_attempts - 1:
return {"success": False, "error": str(e), "fallback_used": True}
# All retries exhausted - Fail Loud (Law 4)
raise ValueError(f"Failed to resolve expression after {max_attempts} 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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- Timing Estimates - Expected latency including fallback scenarios
Related Skills
| Skill | Purpose | |
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
Live References
Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.