n8n-workflow-architect

仓库: promptadvisers/n8n-powerhouse
安装量: 65
排名: #11608

安装

npx skills add https://github.com/promptadvisers/n8n-powerhouse --skill n8n-workflow-architect
n8n Workflow Architect
The Intelligent Automation Architect (IAA) - Strategic guidance for building automation systems that survive production.
When to Use This Skill
Invoke this skill when users:
Want to plan an automation project
- "I need to automate my sales pipeline"
Have multiple services to integrate
- "I use Shopify, Klaviyo, and Notion"
Need architecture decisions
- "Should I use n8n or Python for this?"
Are evaluating feasibility
- "Can I automate X with my current stack?"
Want production-ready guidance
- "How do I make this reliable?"
The Core Philosophy
Viability over Possibility
The gap between what's technically possible and what's actually viable in production is enormous. This skill helps users build systems that:
Won't break at 3 AM on a Saturday
Don't require a PhD to maintain
Respect data security, scale, and state management
Deliver actual business value, not just technical cleverness
Architecture Decision Framework
Step 1: Stack Analysis
When a user mentions their tools, evaluate each for:
Tool Category
Common Examples
n8n Native Support
Auth Complexity
E-commerce
Shopify, WooCommerce, BigCommerce
Yes
OAuth
CRM
HubSpot, Salesforce, Zoho CRM
Yes
OAuth
Marketing
Klaviyo, Mailchimp, ActiveCampaign
Yes
API Key/OAuth
Productivity
Notion, Airtable, Google Sheets
Yes
OAuth
Communication
Slack, Discord, Teams
Yes
OAuth
Payments
Stripe, PayPal, Square
Yes
API Key
Support
Zendesk, Intercom, Freshdesk
Yes
API Key/OAuth
Action
Use search_nodes from n8n MCP to verify node availability. Step 2: Tool Selection Matrix Apply these decision rules: Use n8n When: Condition Why OAuth authentication required n8n manages token lifecycle automatically Non-technical maintainers Visual workflows are self-documenting Multi-day processes with waits Built-in Wait node handles suspension Standard SaaS integrations Pre-built nodes eliminate boilerplate < 5,000 records per execution Within memory limits < 20 nodes of business logic Maintains visual clarity Use Python/Claude Code When: Condition Why

5,000 records to process Stream processing, memory management 20MB files Chunked processing capabilities Complex algorithms Code is more maintainable than 50+ nodes Cutting-edge AI libraries Access to latest packages Heavy data transformation Pandas, NumPy optimization Custom ML models Full Python ecosystem access Use Hybrid (Recommended for Complex Systems): n8n (Orchestration Layer) ├── Webhooks & triggers ├── OAuth authentication ├── User-facing integrations ├── Flow coordination │ └── Calls Python Service (Processing Layer) ├── Heavy computation ├── Complex logic ├── AI/ML operations └── Returns results to n8n Business Stack Quick Assessment When user describes their stack, respond with this analysis: Template Response:

Stack Analysis: [User's Business Type]

Services Identified: 1. ** [Service 1] ** - [Category] - n8n Support: [Yes/Partial/No] 2. ** [Service 2] ** - [Category] - n8n Support: [Yes/Partial/No] ...

Recommended Approach: [n8n / Python / Hybrid] ** Rationale: ** - [Key decision factor 1] - [Key decision factor 2] - [Key decision factor 3]

Integration Complexity: [Low/Medium/High]

Auth complexity: [Simple API keys / OAuth required]

Data volume: [Estimate based on use case]

Processing needs: [Simple transforms / Complex logic]

Next Steps:
1.
[Specific action using other n8n skills]
2.
[Pattern to follow from n8n-workflow-patterns]
3.
[Validation approach from n8n-validation-expert]
Common Business Scenarios
Scenario 1: E-commerce Automation
Stack
Shopify + Klaviyo + Slack + Google Sheets
Verdict
Pure n8n
All services have native nodes
OAuth handled automatically
Standard webhook patterns
Use:
n8n-workflow-patterns
→ webhook_processing
Scenario 2: AI-Powered Lead Qualification
Stack
Typeform + HubSpot + OpenAI + Custom Scoring
Verdict
Hybrid
n8n: Typeform webhook, HubSpot sync, notifications
Python/Code Node: Complex scoring algorithm, AI prompts
Use:
n8n-workflow-patterns
→ ai_agent_workflow
Scenario 3: Data Pipeline / ETL
Stack
PostgreSQL + BigQuery + 50k+ daily records
Verdict
Python with n8n Trigger
n8n: Schedule trigger, success/failure notifications
Python: Batch processing, streaming, transformations
Reason: Memory limits in n8n for large datasets
Scenario 4: Multi-Step Approval Workflow
Stack
Slack + Notion + Email + 3-day wait periods
Verdict
Pure n8n Built-in Wait node for delays Native Slack/Notion integrations Human approval patterns built-in Use: n8n-workflow-patterns → scheduled_tasks Production Readiness Checklist Before any automation goes live, verify: Observability Error notification workflow exists Execution logging to database Health check workflow for critical paths Structured alerting by severity Idempotency Duplicate webhook handling Check-before-create patterns Idempotency keys for payments Safe re-run capability Cost Awareness AI API costs calculated and approved Rate limits documented Caching strategy for repeated calls Model right-sizing (Haiku vs Sonnet vs Opus) Operational Control Kill switch accessible to non-technical staff Approval queues for high-stakes actions Audit trail for all actions Configuration externalized Use n8n-validation-expert skill to validate workflows before deployment. Integration with Other n8n Skills This skill works as the planning layer that coordinates other skills: ┌─────────────────────────────────────────────────────────────┐ │ n8n-workflow-architect │ │ (Strategic Decisions & Planning) │ └─────────────────────────────────────────────────────────────┘ │ ┌────────────────────┼────────────────────┐ ▼ ▼ ▼ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ n8n-workflow- │ │ n8n-node- │ │ n8n-validation- │ │ patterns │ │ configuration │ │ expert │ │ (Architecture) │ │ (Node Setup) │ │ (Quality) │ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ │ │ └────────────────────┼────────────────────┘ ▼ ┌─────────────────────────────────────────────────────────────┐ │ n8n MCP Tools │ │ (search_nodes, validate_workflow, create_workflow, etc.) │ └─────────────────────────────────────────────────────────────┘ Skill Handoff Guide: After Architect Decides... Hand Off To Pattern type identified n8n-workflow-patterns for detailed structure Specific nodes needed n8n-node-configuration for setup Code node required n8n-code-javascript or n8n-code-python Expressions needed n8n-expression-syntax for correct syntax Ready to validate n8n-validation-expert for pre-deploy checks Need node info n8n MCP → get_node_essentials , search_nodes Plan Mode Activation For complex architectural decisions, enter plan mode to: Analyze the full business context Evaluate all integration points Design the data flow architecture Identify failure modes and mitigations Create implementation roadmap Trigger Plan Mode When: User has 3+ services to integrate Unclear whether n8n or Python is better High-stakes automation (payments, customer data) Complex multi-step processes AI/ML components involved Plan Mode Output Structure:

Automation Architecture Plan

  1. Business Context [What problem are we solving?]

  1. Stack Analysis [Each service, its role, integration complexity]

  1. Recommended Architecture [n8n / Python / Hybrid with rationale]

  1. Data Flow Design [Visual representation of the flow]

  1. Implementation Phases Phase 1: [Core workflow] Phase 2: [Error handling & observability] Phase 3: [Optimization & scaling]

  1. Risk Assessment [What could go wrong, how we prevent it]

  1. Maintenance Plan [Who maintains, what skills needed] Quick Decision Tree START: User wants to automate something │ ├─► Does it involve OAuth? ────────────────────► Use n8n │ ├─► Will non-developers maintain it? ──────────► Use n8n │ ├─► Does it need to wait days/weeks? ──────────► Use n8n │ ├─► Processing > 5000 records? ────────────────► Use Python │ ├─► Files > 20MB? ─────────────────────────────► Use Python │ ├─► Cutting-edge AI/ML? ───────────────────────► Use Python │ ├─► Complex algorithm (would need 20+ nodes)? ─► Use Python │ └─► Mix of above? ─────────────────────────────► Use Hybrid MCP Tool Integration Use these n8n MCP tools during architecture planning: Planning Phase MCP Tools to Use Stack analysis search_nodes
  2. verify node availability Pattern selection list_node_templates
  3. find similar workflows Feasibility check get_node_essentials
  4. understand capabilities Complexity estimate get_node_documentation
  5. auth & config needs Template reference get_template
  6. study existing patterns
    Red Flags to Watch For
    Warn users when you see these patterns:
    Red Flag
    Risk
    Recommendation
    "I want AI to do everything"
    Cost explosion, unpredictability
    Scope AI to specific tasks, cache results
    "It needs to process millions of rows"
    Memory crashes
    Python with streaming, not n8n loops
    "The workflow has 50 nodes"
    Unmaintainable
    Consolidate to code blocks or split workflows
    "We'll add error handling later"
    Silent failures
    Build error handling from day one
    "It should work on any input"
    Fragile system
    Define and validate expected inputs
    "The intern will maintain it"
    Single point of failure
    Use n8n for visual clarity, document thoroughly
    Summary
    This skill answers
    "Given my business stack and requirements, what's the smartest way to build this automation?" Key outputs : Stack compatibility analysis n8n vs Python vs Hybrid recommendation Pattern and skill handoffs Production readiness guidance Implementation roadmap via plan mode Works with : All n8n-* skills for implementation details n8n MCP tools for node discovery and workflow creation Plan mode for complex architectural decisions Related Files tool-selection-matrix.md
  7. Detailed decision criteria business-stack-analysis.md
  8. Common SaaS integration guides production-readiness.md
  9. Pre-launch checklist details
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