# N8n Code Javascript > Implements intelligent n8n code javascript with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/n8n-code-javascript` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/n8n-code-javascript` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/n8n-code-javascript/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/n8n-code-javascript --- # 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 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 ```javascript // 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 ```javascript // 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: 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 ## 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. - [n8n Documentation]() - [n8n JavaScript Code Node]() - [n8n Expressions Reference]() - [JavaScript for Automation Scripting (MDN)]() - [n8n Workflow Templates Library]()