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

skills/n8n-validation-expert/ERROR_CATALOG.md

@@ -6,16 +6,16 @@ Comprehensive catalog of n8n validation errors with real examples and fixes.
 
 ## Error Types Overview
 
-From analysis of 19,113 validation errors:
+Common validation errors by priority:
 
-| Error Type | Frequency | Severity | Auto-Fix |
+| Error Type | Priority | Severity | Auto-Fix |
 |---|---|---|---|
-| missing_required | 45% | Error | ❌ |
-| invalid_value | 28% | Error | ❌ |
-| type_mismatch | 12% | Error | ❌ |
-| invalid_expression | 8% | Error | ❌ |
-| invalid_reference | 5% | Error | ❌ |
-| operator_structure | 2% | Warning | ✅ |
+| missing_required | Highest | Error | ❌ |
+| invalid_value | High | Error | ❌ |
+| type_mismatch | Medium | Error | ❌ |
+| invalid_expression | Medium | Error | ❌ |
+| invalid_reference | Low | Error | ❌ |
+| operator_structure | Lowest | Warning | ✅ |
 
 ---
 
@@ -30,7 +30,7 @@ From analysis of 19,113 validation errors:
 - Copying configurations between different operations
 - Switching operations that have different requirements
 
-**Frequency**: 45% of all validation errors
+**Most common validation error**
 
 #### Example 1: Slack Channel Missing
 
@@ -182,7 +182,7 @@ const info = get_node_essentials({
 - Typos in operation names
 - Invalid format for specialized fields (emails, URLs, channels)
 
-**Frequency**: 28% of all validation errors
+**Second most common error**
 
 #### Example 1: Invalid Operation
 
@@ -314,7 +314,7 @@ const info = get_node_essentials({
 - Using expressions where literals are expected
 - JSON serialization issues
 
-**Frequency**: 12% of all validation errors
+**Common error**
 
 #### Example 1: String Instead of Number
 
@@ -450,7 +450,7 @@ const info = get_node_essentials({
 - Referencing non-existent nodes or fields
 - Invalid JavaScript syntax in expressions
 
-**Frequency**: 8% of all validation errors
+**Moderately common**
 
 **Related**: See **n8n Expression Syntax** skill for comprehensive expression guidance
 
@@ -581,7 +581,7 @@ const info = get_node_essentials({
 - Typo in node name
 - Copy-pasting from another workflow
 
-**Frequency**: 5% of all validation errors
+**Less common error**
 
 #### Example 1: Deleted Node Reference
 
@@ -804,7 +804,7 @@ SELECT * FROM users WHERE active = true LIMIT 1000
 
 **Auto-Fix**: ✅ YES - Fixed automatically on workflow save
 
-**Frequency**: 2% of errors (mostly auto-fixed)
+**Rare** (mostly auto-fixed)
 
 #### Fixed Automatically: Binary Operators
 

skills/n8n-validation-expert/FALSE_POSITIVES.md

@@ -10,9 +10,8 @@ When validation warnings are acceptable and how to handle them.
 
 **Key insight**: Not all warnings need to be fixed!
 
-From telemetry analysis:
-- 15,107 validation feedback loops
-- ~40% of warnings are accepted as-is
+Many warnings are context-dependent:
+- ~40% of warnings are acceptable in specific use cases
 - Using `ai-friendly` profile reduces false positives by 60%
 
 ---

skills/n8n-validation-expert/README.md

@@ -10,10 +10,9 @@ Expert guidance for interpreting and fixing n8n validation errors.
 
 ## The Problem This Solves
 
-From analysis of 447,557 MCP tool usage events:
+Validation errors are common:
 
-- **19,113 validation errors** encountered
-- **15,107 validation feedback loops** (79% of errors lead to loops)
+- Validation often requires iteration (79% lead to feedback loops)
 - **7,841 validate → fix cycles** (avg 23s thinking + 58s fixing)
 - **2-3 iterations** average to achieve valid configuration
 

skills/n8n-validation-expert/SKILL.md

@@ -13,10 +13,9 @@ Expert guide for interpreting and fixing n8n validation errors.
 
 **Validate early, validate often**
 
-From telemetry analysis:
-- 19,113 validation errors encountered
-- 15,107 validation feedback loops
-- 7,841 validate → fix cycles
+Validation is typically iterative:
+- Expect validation feedback loops
+- Usually 2-3 validate → fix cycles
 - Average: 23s thinking about errors, 58s fixing them
 
 **Key insight**: Validation is an iterative process, not one-shot!