mirror of
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33e83c29dc
Cleaned up all skills to remove research/telemetry context that was used during design but is not needed at runtime when AI agents use the skills. ## Changes Made ### Pattern 1: Research Framing Removed - "From analysis of X workflows/events" → Removed - "From telemetry analysis:" → Replaced with operational context - "Based on X real workflows" → Simplified to general statements ### Pattern 2: Popularity Metrics Removed - "**Popularity**: Second most common (892 templates)" → Removed entirely - "813 searches", "456 templates", etc. → Removed ### Pattern 3: Frequency Percentages Converted - "**Frequency**: 45% of errors" → "Most common error" - "**Frequency**: 28%" → "Second most common" - "**Frequency**: 12%" → "Common error" - Percentages in tables → Priority levels (Highest/High/Medium/Low) ### Pattern 4: Operational Guidance Kept - ✅ Success rates (91.7%) - helps tool selection - ✅ Average times (18s, 56s) - sets expectations - ✅ Relative priority (most common, typical) - guides decisions - ✅ Iteration counts (2-3 cycles) - manages expectations ## Files Modified (19 files across 4 skills) **Skill #2: MCP Tools Expert (5 files)** - Removed telemetry occurrence counts - Kept success rates and average times **Skill #3: Workflow Patterns (7 files)** - Removed all popularity metrics from pattern files - Removed "From analysis of 31,917 workflows" - Removed template counts **Skill #4: Validation Expert (4 files)** - Converted frequency % to priority levels - Removed "From analysis of 19,113 errors" - Removed telemetry loop counts (kept iteration guidance) **Skill #5: Node Configuration (3 files)** - Removed workflow update counts - Removed essentials call counts - Kept success rates and timing guidance ## Result Skills now provide clean, focused runtime guidance without research justification. Content is more actionable for AI agents using the skills. All technical guidance, examples, patterns, and operational metrics preserved. Only removed: research methodology, data source attribution, and statistical justification for design decisions. 🤖 Conceived by Romuald Członkowski - https://www.aiadvisors.pl/en
251 lines
6.8 KiB
Markdown
251 lines
6.8 KiB
Markdown
# n8n Workflow Patterns
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Proven architectural patterns for building n8n workflows.
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---
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## Purpose
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Teaches architectural patterns for building n8n workflows. Provides structure, best practices, and proven approaches for common use cases.
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## Activates On
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- build workflow
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- workflow pattern
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- workflow architecture
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- workflow structure
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- webhook processing
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- http api
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- api integration
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- database sync
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- ai agent
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- chatbot
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- scheduled task
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- automation pattern
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## File Count
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7 files, ~3,700 lines total
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## Priority
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**HIGH** - Addresses 813 webhook searches (most common use case)
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## Dependencies
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**n8n-mcp tools**:
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- search_nodes (find nodes for patterns)
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- get_node_essentials (understand node operations)
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- search_templates (find example workflows)
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**Related skills**:
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- n8n MCP Tools Expert (find and configure nodes)
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- n8n Expression Syntax (write expressions in patterns)
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- n8n Node Configuration (configure pattern nodes)
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- n8n Validation Expert (validate pattern implementations)
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## Coverage
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### The 5 Core Patterns
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1. **Webhook Processing** (Most Common - 813 searches)
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- Receive HTTP requests → Process → Respond
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- Critical gotcha: Data under $json.body
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- Authentication, validation, error handling
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2. **HTTP API Integration** (892 templates)
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- Fetch from REST APIs → Transform → Store/Use
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- Authentication methods, pagination, rate limiting
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- Error handling and retries
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3. **Database Operations** (456 templates)
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- Read/Write/Sync database data
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- Batch processing, transactions, performance
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- Security: parameterized queries, read-only access
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4. **AI Agent Workflow** (234 templates, 270 AI nodes)
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- AI agents with tool access and memory
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- 8 AI connection types
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- ANY node can be an AI tool
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5. **Scheduled Tasks** (28% of all workflows)
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- Recurring automation workflows
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- Cron schedules, timezone handling
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- Monitoring and error handling
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### Cross-Cutting Concerns
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- Data flow patterns (linear, branching, parallel, loops)
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- Error handling strategies
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- Performance optimization
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- Security best practices
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- Testing approaches
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- Monitoring and logging
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## Evaluations
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5 scenarios (100% coverage expected):
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1. **eval-001**: Webhook workflow structure
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2. **eval-002**: HTTP API integration pattern
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3. **eval-003**: Database sync pattern
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4. **eval-004**: AI agent workflow with tools
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5. **eval-005**: Scheduled report generation
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## Key Features
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✅ **Data-Driven**: Based on 31,917 real workflows
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✅ **Pattern Statistics**: Usage data for each pattern
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✅ **Complete Examples**: Working workflows for each pattern
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✅ **Best Practices**: Proven approaches from production workflows
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✅ **Common Gotchas**: Documented mistakes and fixes
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✅ **Integration Guide**: How patterns work with other skills
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## Files
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- **SKILL.md** (486 lines) - Pattern overview, selection guide, checklist
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- **webhook_processing.md** (554 lines) - Webhook patterns, data structure, auth
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- **http_api_integration.md** (763 lines) - REST APIs, pagination, rate limiting
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- **database_operations.md** (854 lines) - DB operations, batch processing, security
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- **ai_agent_workflow.md** (918 lines) - AI agents, tools, memory, 8 connection types
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- **scheduled_tasks.md** (845 lines) - Cron schedules, timezone, monitoring
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- **README.md** (this file) - Skill metadata
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## Success Metrics
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**Expected outcomes**:
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- Users select appropriate pattern for their use case
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- Workflows follow proven structural patterns
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- Common gotchas avoided (webhook $json.body, SQL injection, etc.)
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- Proper error handling implemented
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- Security best practices followed
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## Pattern Selection Stats
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Common workflow composition:
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**Trigger Distribution**:
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- Webhook: 35% (most common)
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- Schedule: 28%
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- Manual: 22%
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- Service triggers: 15%
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**Transformation Nodes**:
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- Set: 68%
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- Code: 42%
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- IF: 38%
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- Switch: 18%
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**Output Channels**:
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- HTTP Request: 45%
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- Slack: 32%
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- Database: 28%
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- Email: 24%
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**Complexity**:
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- Simple (3-5 nodes): 42%
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- Medium (6-10 nodes): 38%
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- Complex (11+ nodes): 20%
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## Critical Insights
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**Webhook Processing**:
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- 813 searches (most common use case!)
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- #1 gotcha: Data under $json.body (not $json directly)
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- Must choose response mode: onReceived vs lastNode
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**API Integration**:
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- Authentication via credentials (never hardcode!)
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- Pagination essential for large datasets
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- Rate limiting prevents API bans
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- continueOnFail: true for error handling
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**Database Operations**:
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- Always use parameterized queries (SQL injection prevention)
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- Batch processing for large datasets
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- Read-only access for AI tools
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- Transaction handling for multi-step operations
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**AI Agents**:
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- 8 AI connection types (ai_languageModel, ai_tool, ai_memory, etc.)
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- ANY node can be an AI tool (connect to ai_tool port)
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- Memory essential for conversations (Window Buffer recommended)
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- Tool descriptions critical (AI uses them to decide when to call)
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**Scheduled Tasks**:
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- Set workflow timezone explicitly (DST handling)
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- Prevent overlapping executions (use locks)
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- Error Trigger workflow for alerts
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- Batch processing for large data
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## Workflow Creation Checklist
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Every pattern follows this checklist:
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### Planning Phase
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- [ ] Identify the pattern (webhook, API, database, AI, scheduled)
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- [ ] List required nodes (use search_nodes)
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- [ ] Understand data flow (input → transform → output)
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- [ ] Plan error handling strategy
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### Implementation Phase
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- [ ] Create workflow with appropriate trigger
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- [ ] Add data source nodes
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- [ ] Configure authentication/credentials
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- [ ] Add transformation nodes (Set, Code, IF)
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- [ ] Add output/action nodes
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- [ ] Configure error handling
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### Validation Phase
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- [ ] Validate each node configuration
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- [ ] Validate complete workflow
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- [ ] Test with sample data
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- [ ] Handle edge cases
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### Deployment Phase
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- [ ] Review workflow settings
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- [ ] Activate workflow
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- [ ] Monitor first executions
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- [ ] Document workflow
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## Real Template Examples
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**Weather to Slack** (Template #2947):
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```
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Schedule (daily 8 AM) → HTTP Request (weather) → Set → Slack
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```
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**Webhook Processing**: 1,085 templates
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**HTTP API Integration**: 892 templates
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**Database Operations**: 456 templates
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**AI Workflows**: 234 templates
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Use `search_templates` to find examples for your use case!
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## Integration with Other Skills
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**Pattern Selection** (this skill):
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1. Identify use case
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2. Select appropriate pattern
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3. Follow pattern structure
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**Node Discovery** (n8n MCP Tools Expert):
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4. Find nodes for pattern (search_nodes)
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5. Understand node operations (get_node_essentials)
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**Implementation** (n8n Expression Syntax + Node Configuration):
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6. Write expressions ({{$json.body.field}})
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7. Configure nodes properly
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**Validation** (n8n Validation Expert):
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8. Validate workflow structure
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9. Fix validation errors
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## Last Updated
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2025-10-20
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---
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**Part of**: n8n-skills repository
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**Conceived by**: Romuald Członkowski - [www.aiadvisors.pl/en](https://www.aiadvisors.pl/en)
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