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refactor: Remove research context from skill content
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
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# n8n Workflow Patterns
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Proven architectural patterns from real n8n workflows.
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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 based on analysis of 31,917 real workflows. Provides structure, best practices, and proven approaches for common use cases.
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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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@@ -121,7 +121,7 @@ Teaches architectural patterns for building n8n workflows based on analysis of 3
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## Pattern Selection Stats
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From analysis of 31,917 workflows:
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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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@@ -5,7 +5,7 @@ description: Proven workflow architectural patterns from real n8n workflows. Use
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# n8n Workflow Patterns
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Proven architectural patterns from 31,917 real n8n workflows.
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Proven architectural patterns for building n8n workflows.
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---
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@@ -13,7 +13,7 @@ Proven architectural patterns from 31,917 real n8n workflows.
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Based on analysis of real workflow usage:
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1. **[Webhook Processing](webhook_processing.md)** (Most Common - 813 searches)
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1. **[Webhook Processing](webhook_processing.md)** (Most Common)
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- Receive HTTP requests → Process → Output
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- Pattern: Webhook → Validate → Transform → Respond/Notify
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@@ -246,10 +246,10 @@ These skills work together with Workflow Patterns:
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## Pattern Statistics
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From analysis of 31,917 workflows:
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Common workflow patterns:
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**Most Common Triggers**:
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1. Webhook (813 searches) - 35%
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1. Webhook - 35%
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2. Schedule (periodic tasks) - 28%
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3. Manual (testing/admin) - 22%
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4. Service triggers (Slack, email, etc.) - 15%
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- Nodes: Schedule → HTTP Request (weather API) → Set → Slack
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- Complexity: Simple (4 nodes)
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**Webhook Processing Templates**: 1,085 templates
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**Webhook Processing**: Most common pattern
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- Most common: Form submissions, payment webhooks, chat integrations
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**HTTP API Templates**: 892 templates
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**HTTP API**: Common pattern
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- Most common: Data fetching, third-party integrations
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**Database Templates**: 456 templates
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**Database Operations**: Common pattern
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- Most common: ETL, data sync, backup workflows
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**AI Templates**: 234 templates
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**AI Agents**: Growing in usage
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- Most common: Chatbots, content generation, data analysis
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Use `search_templates` and `get_template` from n8n-mcp tools to find examples!
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**Key Points**:
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1. **5 core patterns** cover 90%+ of workflow use cases
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2. **Webhook processing** is the most common (813 searches)
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2. **Webhook processing** is the most common pattern
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3. Use the **workflow creation checklist** for every workflow
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4. **Plan pattern** → **Select nodes** → **Build** → **Validate** → **Deploy**
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5. Integrate with other skills for complete workflow development
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**Use Case**: Build AI agents with tool access, memory, and reasoning capabilities.
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**Popularity**: Growing rapidly (234 templates, 270 AI-capable nodes)
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---
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## Pattern Structure
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**Use Case**: Read, write, sync, and manage database data in workflows.
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**Popularity**: Third most common (456 templates)
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---
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## Pattern Structure
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**Use Case**: Fetch data from REST APIs, transform it, and use it in workflows.
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**Popularity**: Second most common (892 templates)
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---
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## Pattern Structure
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**Use Case**: Recurring automation workflows that run automatically on a schedule.
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**Popularity**: Second most common trigger type (28% of workflows)
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---
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## Pattern Structure
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**Use Case**: Receive HTTP requests from external systems and process them instantly.
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**Popularity**: Most searched pattern (813 webhook searches)
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---
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## Pattern Structure
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