paulpas N8N Code Javascript
Orchestrates intelligent skill selection and execution for n8n code javascript 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
// n8n Code Node: Robust Data Processing Pipeline
// Implements input validation, transformation, and safe output formatting
function processWorkflowData(items) {
const validatedItems = [];
const errors = [];
for (let i = 0; i < items.length; i++) {
const item = items[i];
try {
// Law 2: Parse at boundary - validate required fields
if (!item.json || !item.json.sourceId || !item.json.payload) {
throw new Error(`Missing required fields at index ${i}`);
}
// Law 3: Atomic Predictability - return new structures
const processed = {
sourceId: String(item.json.sourceId),
payload: JSON.parse(JSON.stringify(item.json.payload)), // deep clone
processedAt: new Date().toISOString(),
status: 'success'
};
// Domain-specific transformation logic
if (processed.payload.type === 'event') {
processed.payload.timestamp = new Date(processed.payload.timestamp).getTime();
}
validatedItems.push({ json: processed });
} catch (err) {
// Law 4: Fail fast with descriptive errors
errors.push({
json: { error: err.message, index: i, original: item.json },
pairedItem: { item: i }
});
}
}
// Return structured output for n8n execution
return {
items: validatedItems,
errors: errors.length > 0 ? errors : undefined
};
}
// n8n execution entry point
const inputData = $input.all();
const result = processWorkflowData(inputData);
return result.items;
Pattern 2: Execution with Fallback
// n8n Code Node: Resilient Execution with Fallback Chain
// Handles API calls, retries, and graceful degradation
async function executeWithFallback(config) {
const { endpoint, fallbackEndpoint, maxRetries = 2, timeout = 5000 } = config;
let lastError = null;
for (let attempt = 0; attempt <= maxRetries; attempt++) {
try {
// Law 1: Early exit on invalid config
if (!endpoint || typeof endpoint !== 'string') {
throw new Error('Invalid endpoint configuration');
}
// Domain-specific: Execute primary workflow step
const response = await fetch(endpoint, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(config.payload),
signal: AbortSignal.timeout(timeout)
});
if (!response.ok) {
throw new Error(`HTTP ${response.status}: ${response.statusText}`);
}
const data = await response.json();
return { success: true, data, attempts: attempt + 1 };
} catch (err) {
lastError = err;
// Law 4: Fail loud on invalid states, retry on transient
if (err.name === 'AbortError' || err.status >= 500) {
if (attempt === maxRetries) break;
await new Promise(r => setTimeout(r, Math.pow(2, attempt) * 1000));
continue;
}
// Non-retryable error - fail immediately
break;
}
}
// Fallback Chain: Attempt secondary endpoint
if (fallbackEndpoint && lastError) {
try {
const fallbackResponse = await fetch(fallbackEndpoint, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(config.fallbackPayload || config.payload)
});
const fallbackData = await fallbackResponse.json();
return { success: true, data: fallbackData, attempts: 'fallback', error: lastError.message };
} catch (fallbackErr) {
// Final failure - return structured error for n8n UI
return { success: false, error: fallbackErr.message, lastAttemptError: lastError.message };
}
}
return { success: false, error: lastError.message };
}
// n8n execution context
const workflowConfig = {
endpoint: $input.item.json.apiEndpoint,
payload: $input.item.json.requestBody,
fallbackEndpoint: $input.item.json.fallbackApi || null,
maxRetries: 2
};
const executionResult = await executeWithFallback(workflowConfig);
return [{ json: executionResult }];
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.