Clean up all README files to focus on user value rather than research metrics.
Remove telemetry numbers and research context that isn't useful for end users.
## Changes
**Main README.md**:
- Removed "Based on 447,557 real MCP tool usage events" section
- Replaced failure rate metrics with user benefits
- Removed entire "Data-Driven Design" section with telemetry statistics
- Fixed all GitHub links to use czlonkowski/n8n-mcp
- Updated "Repository Stats" to "What's Included" with user-focused content
**dist/README.md**:
- Changed "HIGHEST PRIORITY" to "recommended to install first"
- Added link to n8n-mcp repository
- More user-friendly language throughout
**Skill README.md files**:
- n8n-mcp-tools-expert: Removed "447,557 events", "20% failure rate" metrics
- n8n-workflow-patterns: Removed "Based on 31,917 real workflows"
- n8n-validation-expert: Removed "From 7,841 validate → fix cycles"
- Replaced frequency percentages with priority levels (Highest/High/Medium/Low)
- Reframed "Success Metrics" as "What You'll Learn"
- Changed "Critical Insights from telemetry" to "Key Insights" for users
## Kept What Matters
- Template counts (2,653+) - this is a feature, not research
- Node counts (525+) - this is a feature
- Practical insights (validation takes 2-3 iterations, false positives exist)
- Best practices and common patterns
## Result
Documentation now focuses on what users need to know to use the skills
effectively, rather than the research that informed their creation.
All distribution packages regenerated with cleaned documentation.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Conceived by Romuald Członkowski - https://www.aiadvisors.pl/en
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
