gh-hirefrank-hirefrank-marketplace-plugins-claude-skills-analyzer/skills/workflow-pattern-analyzer

Workflow Pattern Analyzer

A web-compatible Custom Skill that brings export-quality conversation analysis to Claude's web interface. Analyzes recent conversation history using native chat tools to identify recurring patterns and generate evidence-based Custom Skills recommendations.

Why This Skill?

The Bridge Between Two Worlds:

• Export-based plugin (/analyze-skills): Comprehensive but requires Claude Code + JSON exports
• skill-idea-generator: Web-friendly but lacks statistical rigor
• workflow-pattern-analyzer: Best of both - rigorous analysis accessible anywhere

Key Features

🌐 Web Interface Compatible

• No conversation exports required
• Uses recent_chats and conversation_search tools
• Works in Claude.ai web interface or Claude Code

📊 Statistical Rigor

• 5-dimensional scoring framework (0-10 scale each):
  • Frequency analysis
  • Consistency evaluation
  • Complexity assessment
  • Time savings calculation
  • Error reduction potential
• Composite scoring (0-50 total) for prioritization
• Statistical validation with significance thresholds

🎯 Comprehensive Analysis

• Pattern discovery across multiple dimensions (explicit, implicit, domain, temporal)
• Relationship mapping and overlap detection
• Smart consolidation strategies
• Evidence-based recommendations with conversation excerpts

📦 Complete Outputs

• Detailed analysis reports with pattern evidence
• Prioritization matrix (frequency vs impact)
• Ready-to-use skill specifications
• Implementation roadmap

When to Use

Perfect for:

• Web interface users who can't run /analyze-skills
• Quick pattern identification without export overhead
• Iterative skill discovery (start small, expand as needed)
• Users who want analysis rigor without technical setup

Use the export plugin instead when:

• You have Claude/ChatGPT conversation exports available
• You need cross-platform analysis (Claude + ChatGPT)
• You want comprehensive historical analysis (100+ conversations)
• You need incremental processing for large datasets

Usage Guide

Quick Start

Simply say:

• "Analyze my conversation patterns"
• "What workflows should I automate?"
• "Find skill opportunities in my recent chats"
• "Identify my most common requests"

Analysis Depth Options

1. Quick Scan (20-30 conversations, ~2-3 min)

"Do a quick scan of my recent conversations"

Best for: Immediate insights, identifying top 1-2 patterns

2. Standard Analysis (50-75 conversations, ~5-7 min)

"Analyze my conversation history for patterns"

Best for: Comprehensive pattern detection, multiple skill opportunities

3. Deep Dive (100+ conversations, ~10-15 min)

"Do a comprehensive analysis of my workflows"

Best for: Full workflow mapping, temporal trends, strategic insights

4. Targeted Search (variable)

"Find patterns in my coding conversations"
"Analyze how I use you for writing tasks"

Best for: Domain-specific skill discovery

Understanding the Output

Score Interpretation:

• 40-50 (Critical): Implement immediately - highest ROI
• 30-39 (High): Strong candidates for skill creation
• 20-29 (Medium): Consider for automation
• 10-19 (Low): Defer or use simple prompt templates
• 0-9 (Not Viable): Not worth skill automation

Prioritization Matrix:

VALUE/IMPACT
     │
HIGH │  Quick Wins      Strategic
     │  [Immediate ROI]  [Critical but complex]
     │
LOW  │  Automate        Defer
     │  [Nice-to-have]  [Not worth it]
     │
     └─────────────────────────
          LOW  FREQUENCY  HIGH

Example Workflows

Scenario 1: First-Time User

User: "I want to find out what I should automate"

Skill Output:

• Quick scan of 30 recent conversations
• Top 3 patterns identified with scores
• Evidence excerpts from actual conversations
• Recommendation for next steps (expand analysis or build skills)

Scenario 2: Domain-Specific Analysis

User: "Find patterns in my coding work"

Skill Output:

• Targeted search of coding-related conversations
• Domain-specific patterns (code review, documentation, debugging)
• Frequency and consistency scores for each pattern
• Skill specifications tailored to development workflows

Scenario 3: Comprehensive Workflow Audit

User: "Do a deep analysis of everything I do"

Skill Output:

• Analysis of 100+ conversations across 3 months
• Full pattern taxonomy (15+ patterns identified)
• Prioritization matrix with 6-8 skill recommendations
• Implementation roadmap with phased approach
• Complete skill packages ready to deploy

What Makes This Different?

vs. skill-idea-generator

Feature	skill-idea-generator	workflow-pattern-analyzer
Approach	Conversational suggestions	Statistical analysis
Scoring	Qualitative	Quantitative (0-50 scale)
Evidence	Minimal	Detailed conversation excerpts
Output	Ideas + sketches	Complete skill packages
Best for	Inspiration, brainstorming	Evidence-based decisions

vs. Export-Based Plugin

Feature	Export Plugin	workflow-pattern-analyzer
Platform	Claude Code only	Web + Claude Code
Setup	Requires JSON exports	Zero setup
Data Scope	Complete history	Recent accessible history
Cross-platform	Claude + ChatGPT	Claude only
Analysis Depth	Comprehensive	Extensive (within tool limits)
Best for	Historical analysis	Quick insights

Quality Standards

Pattern Validation:

• Minimum 3 instances OR >5% of conversations
• 70%+ consistency across instances
• 2-3 conversation excerpts as evidence

• 30 min/month cumulative time savings

Skill Recommendations:

• Maximum 8-10 skills (focus on ROI)
• Clear differentiation between skills
• Evidence-based design from actual usage
• Practical focus on time/quality impact

Analysis Rigor:

• No generic patterns (avoid "writing", "analysis")
• Validated frequencies within sample
• Temporal awareness (emerging/stable/declining)
• User context consideration

Advanced Usage

Incremental Analysis

Start with quick scan, expand iteratively:

1. Quick scan (30 conversations) → Identify top pattern
2. Generate skill for top pattern → Deploy and test
3. Standard analysis (50-75 conversations) → Find next opportunities
4. Deep dive (100+ conversations) → Complete workflow mapping

Adjusting Scoring Weights

Request custom prioritization:

"Analyze my patterns but prioritize time savings over frequency"
"Focus on high-complexity patterns even if they're less frequent"

Domain-Focused Batches

Analyze specific workflow areas:

"Analyze my business communication patterns"
"Find patterns in my technical writing"
"What do I repeatedly do for project planning?"

Technical Details

Data Collection Strategy

• Broad sampling: Multiple recent_chats calls with varied parameters
• Temporal distribution: Sample across different time periods
• Topic exploration: conversation_search for discovered domains
• Smart batching: Balance coverage with efficiency

Pattern Detection Methods

• Explicit markers: Repeated phrases, formatting instructions
• Implicit workflows: Multi-turn structures, refinement cycles
• Domain clustering: Topic frequency and task type analysis
• Temporal patterns: Recurring tasks, event-driven workflows

Scoring Methodology

Each pattern scored 0-10 across 5 dimensions:

1. Frequency: Occurrence rate in conversation sample
2. Consistency: Similarity of requirements across instances
3. Complexity: Steps, decision points, cognitive load
4. Time Savings: Minutes saved per use × frequency
5. Error Reduction: Quality improvement potential

Composite score (0-50) determines priority classification.

Limitations

Compared to Export-Based Analysis:

• ❌ Can't analyze ChatGPT conversations
• ❌ Limited to accessible recent history (API constraints)
• ❌ No cross-platform deduplication
• ❌ Can't process 1000+ conversation datasets efficiently
• ❌ No incremental processing log

Inherent Constraints:

• Requires 10+ conversations for basic analysis
• Pattern detection accuracy improves with more data
• Very old conversations may not be accessible via tools
• Analysis time scales with conversation depth

Best Practices

For Accurate Results:

1. Run analysis after accumulating 30+ conversations
2. Use targeted searches for specific domains
3. Request deep dive for comprehensive insights
4. Provide feedback on detected patterns (accuracy validation)

For Skill Generation:

1. Start with top 3-5 highest-scoring patterns
2. Test generated skills before building more
3. Iterate based on actual usage
4. Re-run analysis monthly as patterns evolve

For Efficiency:

1. Use quick scans for regular check-ins
2. Save deep dive for quarterly workflow audits
3. Focus targeted searches on specific pain points
4. Combine analysis with skill building in same session

Example Output Structure

# Workflow Pattern Analysis Report
**Analysis Date**: 2025-01-23
**Conversations Analyzed**: 75 conversations (3 months)
**Patterns Identified**: 12 patterns
**Skills Recommended**: 5 skills

## 🔥 HIGH-PRIORITY OPPORTUNITIES

### 1. Email Response Composer
**Score: 42/50** (Frequency: 9/10, Consistency: 9/10, Complexity: 6/10, Time: 10/10, Error: 8/10)

**Pattern Description**: You regularly draft professional emails with specific tone and structure requirements

**Evidence**:
- Found in 14 conversations (18.7% of sample)
- First seen: Oct 15, Most recent: Jan 20
- Average time per instance: 15 minutes
- Total time savings potential: 210 min/month

**Example Occurrences**:
1. Jan 18: "Draft an email to client about project delay..."
2. Jan 12: "Write a professional response to vendor inquiry..."
3. Jan 5: "Compose email to team about Q1 objectives..."

**Proposed Skill**: Professional email composer with tone control, structure templates, and action item extraction

**Implementation Priority**: Immediate (Highest ROI)

---

[4 more patterns with detailed breakdowns]

## 💡 MODERATE OPPORTUNITIES
[3 patterns, briefer format]

## ⏸️  DEFERRED PATTERNS
[4 patterns that didn't meet thresholds]

## 📊 PRIORITIZATION MATRIX
[Visual classification of patterns]

## 🚀 IMPLEMENTATION ROADMAP
Week 1: Build Email Response Composer + Meeting Notes Structurer
Week 2: Test and refine initial skills
Week 3: Build Code Review Checklist + API Documentation Humanizer
Week 4: Evaluate usage, iterate, consider remaining patterns

Contributing

Found a bug or have suggestions for improving the analysis methodology?

• Open an issue: GitHub Issues
• Discuss improvements: GitHub Discussions

License

MIT License - see repository root for details

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Built to bridge web accessibility with export-quality analysis rigor