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---
name: conversation-analyzer
description: Analyzes AI conversation exports to identify recurring patterns and generate custom Claude Skills. Use when analyzing conversation data, identifying workflow patterns, or creating reusable AI skills from usage history.
---
# Conversation Analyzer
## Instructions
This skill provides specialized capability for analyzing AI conversation exports (Claude, ChatGPT) to identify recurring patterns and generate reusable Custom Skills.
1. **Data Processing & Pattern Discovery**
- Auto-detect platform format (Claude vs ChatGPT exports)
- Parse conversation histories, project data, and user information
- Extract expertise indicators and usage patterns
- Categorize patterns by domain (coding, writing, business, analysis)
- Identify task types (creation, transformation, analysis, troubleshooting)
2. **Frequency & Temporal Analysis**
- Count pattern occurrences across conversation history
- Calculate temporal distribution and frequency trends
- Cross-reference with project data for validation
- Assess business impact and time investment patterns
3. **Skill-Worthiness Evaluation** (0-10 scale scoring):
- **Frequency**: How often does this task occur?
- **Consistency**: How similar are requirements each time?
- **Complexity**: Would a skill meaningfully improve quality?
- **Time savings**: How much effort would a skill save?
- **Error reduction**: Common pitfalls a skill could prevent?
4. **Cross-Platform Deduplication** (when both platforms present):
- Detect semantic similarity across platforms
- Identify cross-platform workflows vs genuine duplicates
- Merge evidence while preserving platform preferences
- Recalculate frequencies after deduplication
5. **Skill Generation & Optimization**
- Create prioritization matrix (frequency vs. value/impact)
- Resolve overlaps and optimize skill boundaries
- Generate complete skill packages with YAML frontmatter
- Provide implementation roadmap and testing guidance
## Quality Standards
- Focus on patterns with >5% conversation frequency
- Require 70%+ consistency across pattern instances
- Target >30 min/week time savings potential
- Maximum 12 skills total (recommend prioritizing top 5-8)
- Platform-agnostic skill design
- Evidence-based recommendations with conversation excerpts
## Examples
### Analyzing Claude Export
**User Request**: "Analyze my Claude conversations to find skill opportunities"
**Response**:
```
I'll analyze your Claude conversation export to identify recurring patterns. Let me:
1. Parse your conversations.json and projects.json files
2. Identify frequent task patterns across your usage
3. Evaluate each pattern for skill-worthiness
4. Generate prioritized recommendations for Custom Skills
Please ensure your export files are in data-exports/claude/ directory.
```
### Cross-Platform Analysis
**User Request**: "I have both Claude and ChatGPT exports - analyze them together"
**Response**:
```
I'll perform cross-platform analysis to identify your workflow patterns:
1. Process both Claude and ChatGPT conversation data
2. Detect and handle cross-platform duplicates
3. Identify platform preferences for different task types
4. Create unified skills that work across AI platforms
5. Provide platform-agnostic skill recommendations
This will give you the most accurate picture of your AI usage patterns.
```

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# Workflow Pattern Analyzer (Web Compatible)
**Single-file, web-compatible version** of the workflow pattern analyzer skill for use in Claude.ai web interface.
## Purpose
This is a **self-contained version** of the workflow-pattern-analyzer skill designed specifically for platforms that require single-file uploads:
- ✅ **Claude.ai web interface** (upload as zip)
- ✅ **Claude API** (single skill upload)
- ✅ **Claude Code** (works but modular version is preferred)
## Differences from Modular Version
| Feature | Modular Version | Web-Compatible Version |
|---------|----------------|------------------------|
| **File structure** | References `../../shared/analysis-methodology.md` | Complete methodology embedded |
| **Maintenance** | Shared methodology, no duplication | Self-contained, fully duplicated |
| **Platform support** | Claude Code only | All platforms |
| **File size** | Smaller (references shared files) | Larger (includes everything) |
| **Updates** | Update shared file once | Must update each skill individually |
## Usage
### In Claude.ai Web Interface
1. Download `SKILL.md` from this directory
2. Create a zip file containing just `SKILL.md`
3. Upload via Settings > Features > Custom Skills
4. Use with phrases like:
- "Analyze my conversation patterns"
- "What workflows should I automate?"
- "Find skill opportunities in my recent chats"
### In Claude API
```python
# Upload the skill
with open('SKILL.md', 'r') as f:
skill_content = f.read()
response = client.skills.create(
name="workflow-pattern-analyzer-web",
content=skill_content
)
```
### In Claude Code
Place `SKILL.md` in your skills directory, though the modular version is recommended for Claude Code environments.
## Full Feature Parity
This web-compatible version includes **100% of the functionality** from the modular version:
### ✅ Complete Analysis Framework
- 7-phase methodology (data collection → skill generation)
- 5-dimensional scoring system (0-50 composite scale)
- Pattern discovery across 4 detection methods
- Statistical validation with significance thresholds
### ✅ Full Output Capabilities
- Detailed analysis reports with evidence
- Prioritization matrix (frequency vs impact)
- Complete skill package generation
- Interactive follow-up options
### ✅ Quality Standards
- Pattern validation requirements
- Skill consolidation rules
- Anti-patterns guidance
- Progressive disclosure strategy
### ✅ Examples & Usage Patterns
- Quick scan (30 conversations)
- Standard analysis (50-75 conversations)
- Deep dive (100+ conversations)
- Targeted domain searches
## Comparison with Export-Based Analysis
| Feature | Export Analysis (`/analyze-skills`) | Tool-Based Analysis (this skill) |
|---------|-------------------------------------|----------------------------------|
| **Platform** | Claude Code only | All platforms (web + CLI) |
| **Data source** | JSON export files | `recent_chats` + `conversation_search` |
| **History scope** | Complete history | Accessible recent history |
| **Cross-platform** | Claude + ChatGPT | Claude only |
| **Setup required** | Export files, directory structure | None |
| **Analysis depth** | Comprehensive (100+ conversations) | Extensive (within tool limits) |
| **Best for** | Historical analysis, large datasets | Quick insights, web users |
## When to Use This Version
**✅ Use this web-compatible version when:**
- Working in Claude.ai web interface
- Want zero-setup analysis
- Need quick pattern identification
- Prefer single-file skills
- Using Claude API with skill uploads
**🔧 Use the modular version when:**
- Working in Claude Code
- Want maintainable architecture
- Building multiple related skills
- Prefer shared methodology approach
## Technical Implementation
This version embeds the complete shared methodology directly in the SKILL.md file:
```markdown
---
name: workflow-pattern-analyzer-web
description: [Complete trigger description]
---
# Workflow Pattern Analyzer (Web Compatible)
## Instructions
[Complete 7-phase analysis framework]
### Phase 1: Data Collection Strategy
[Full methodology embedded]
### Phase 2: Pattern Discovery & Classification
[All 4 detection methods included]
### Phase 3-7: [Complete framework]
[No external references - everything included]
```
**File size**: ~15KB (vs ~3KB for modular version)
**Dependencies**: None (vs modular version's shared file dependency)
**Compatibility**: Universal (vs Claude Code only)
## Maintenance Notes
When updating methodology:
1. ✅ Update `shared/analysis-methodology.md` (affects modular version)
2. ⚠️ **Also update this file** (web-compatible version won't auto-sync)
Consider this trade-off between maintainability and compatibility when choosing which version to use.
## Installation & Testing
1. **Download**: Get `SKILL.md` from this directory
2. **Verify**: Check YAML frontmatter is valid
3. **Test**: Upload to your preferred platform
4. **Validate**: Try with "Analyze my conversation patterns"
The skill will automatically detect analysis scope and provide depth options (quick scan, standard analysis, deep dive, targeted search).
---
**Built for universal compatibility while maintaining full analytical rigor**

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---
name: workflow-pattern-analyzer-web
description: Analyzes recent conversation history using chat tools to identify recurring workflow patterns and generate Custom Skills recommendations with statistical rigor. Use when users request workflow analysis, pattern identification, skill generation suggestions, or automation opportunities based on their AI usage patterns without requiring conversation exports.
---
# Workflow Pattern Analyzer (Web Compatible)
## Instructions
This skill provides comprehensive conversation pattern analysis using Claude's native chat history tools (`recent_chats` and `conversation_search`) to identify skill-worthy automation opportunities with statistical rigor.
**Core Capabilities:**
- Web interface compatible (no exports required)
- Statistical pattern validation and scoring
- Frequency analysis and temporal tracking
- Evidence-based skill recommendations
- Complete skill package generation
**Compatible with:** Claude.ai web interface, Claude Code, API
**How Analysis Works:**
- **No scripts or Python files**: This is a pure prompt-based analysis using Claude's native capabilities
- **Full content analysis**: Examines complete conversation content, messages, and patterns (not just titles or names)
- **Thread names**: Renaming conversations has minimal impact - analysis focuses on actual message content and patterns
- **Domain discovery**: Categories emerge from your actual usage data, not forced into predefined buckets
- **Data-driven approach**: Identifies YOUR specific patterns (recipes, image prompting, game design, etc.) rather than assuming business/coding focus
## Analysis Framework
### Phase 1: Data Collection Strategy
**Determine Analysis Scope:**
Ask user: "How deep should I analyze your conversation history?"
**Options:**
- **Quick Scan** (20-30 conversations, ~2-3 min): Recent patterns and immediate opportunities
- **Standard Analysis** (50-75 conversations, ~5-7 min): Comprehensive pattern detection
- **Deep Dive** (100+ conversations, ~10-15 min): Full workflow mapping with temporal trends
- **Targeted Search** (variable): Focus on specific topics or time periods
**Data Collection Process:**
1. **Broad Sampling**: Use `recent_chats(n=30)` multiple times with varied parameters to get diverse coverage
2. **Temporal Distribution**: Sample conversations across different time periods (recent, 1 week ago, 1 month ago)
3. **Topic Exploration**: Use `conversation_search` for domains mentioned by user or detected in initial sampling
4. **Depth vs Breadth**: Balance comprehensive coverage with processing efficiency
### Phase 2: Pattern Discovery & Classification
**Extract patterns using these detection methods:**
#### A. Explicit Pattern Markers
- **Repeated phrasing**: "format this as...", "make it more...", "apply X style"
- **Consistent request structures**: "create a [X] that does [Y]"
- **Recurring formatting instructions**: tables, bullet lists, specific structures
- **Tone/voice adjustments**: "more casual", "add enthusiasm", "formal version"
#### B. Implicit Workflow Patterns
- **Multi-turn conversation structures**: Same workflow across different topics
- **Iterative refinement sequences**: Request → feedback → revision cycles
- **Context re-explanation**: Same background info provided repeatedly
- **Problem-solving approaches**: Consistent debugging/analysis methodologies
#### C. Domain Discovery (Data-Driven)
- **Let domains emerge from the data** - Do NOT pre-categorize into standard domains
- **Topic frequency analysis**: Extract actual subject matter from conversations
- Examples of specialized domains: recipe transcription, cannabis strains, image prompting, game design, book summaries
- Examples of traditional domains: coding, business strategy, creative writing, data analysis, technical writing
- **Task type patterns**: Identify the action types that appear (creation, transformation, analysis, troubleshooting, curation, etc.)
- **Niche specialization detection**: Look specifically for narrow, specialized topics with high engagement
- **Cross-domain workflows**: Patterns that span multiple topics
- **Domain diversity scoring**: Reward finding 8-15 distinct domains vs. forcing into 3-4 buckets
**CRITICAL**: Avoid fitting patterns into predefined categories. Each user's conversation history will have unique domains based on their actual usage.
**Terminal Output - Domain Diversity Visualization:**
After completing pattern discovery, display an ASCII chart showing domain distribution:
```
📊 Domain Distribution Analysis
Business & Strategy ████████████░░░░░░░░ 12 patterns (32%)
Creative & Writing ██████████░░░░░░░░░░ 10 patterns (27%)
Image Prompting ████████░░░░░░░░░░░░ 8 patterns (22%)
Learning & Education ████░░░░░░░░░░░░░░░░ 4 patterns (11%)
Recipe & Cooking ██░░░░░░░░░░░░░░░░░░ 2 patterns (5%)
Gaming & Design █░░░░░░░░░░░░░░░░░░░ 1 pattern (3%)
✅ Domain Diversity: 6 distinct topic areas detected
✅ No predefined categorization - domains emerged from your data
```
This validates data-driven discovery of diverse patterns.
#### D. Niche & Specialized Pattern Detection
**Explicitly search for underrepresented domains:**
- **Hobbyist domains**: Recipes, cocktails, cannabis, gardening, gaming, fitness, travel planning
- **Creative domains**: Story writing, worldbuilding, character development, art direction, music composition
- **Prompt engineering**: Image generation (Midjourney, Stable Diffusion, DALL-E), video generation, AI art workflows
- **Learning & education**: Book summaries, concept explanations, study guides, teaching materials
- **Personal organization**: Resume writing, cover letters, personal branding, goal setting
- **Entertainment & media**: Game design, narrative design, content creation, video scripts
- **Wellness & lifestyle**: Meal planning, workout routines, meditation guides, habit tracking
**Detection strategy:**
- Look for concentrated clusters of 5+ conversations on the same narrow topic
- Identify specialized vocabulary/jargon (strain names, recipe terms, art styles, game mechanics)
- Find recurring templates/formats specific to that domain
- Don't dismiss low-frequency patterns if they show high consistency and complexity
- Pay special attention to patterns that appear in conversation titles or search results
- Consider that niche patterns may have lower frequency but higher value due to specialization
**Quality indicators for niche patterns:**
- Consistent terminology and domain-specific language
- Recurring output formats or structures
- User demonstrates growing expertise over time
- High engagement (longer conversations, multiple refinements)
- Clear workflow or methodology emerging
#### E. Temporal Patterns
- **Weekly/monthly recurring tasks**: Reports, summaries, check-ins
- **Event-driven patterns**: Meeting prep, post-mortems, launches
- **Seasonal trends**: Quarterly reviews, annual planning
- **Frequency trends**: Increasing/stable/decreasing over time
### Phase 3: Frequency Analysis & Validation
For each identified pattern, calculate:
#### Occurrence Metrics
- **Absolute frequency**: Total instances found in analyzed conversations
- **Relative frequency**: Percentage of conversations containing pattern
- **Temporal distribution**: First occurrence, most recent, clustering
- **Consistency score**: Similarity across pattern instances (0-100%)
#### Statistical Validation
- **Significance threshold**: Pattern must appear in >5% of conversations OR >3 absolute instances
- **Consistency requirement**: 70%+ similarity in requirements/structure across instances
- **Sample size consideration**: Adjust thresholds based on total conversations analyzed
#### Evidence Collection
- Extract 2-4 representative conversation excerpts per pattern
- Note variation types (what changes vs what stays constant)
- Document user refinement patterns (common adjustments made)
### Phase 4: Skill-Worthiness Scoring (0-10 Scale)
**Use extended reasoning to evaluate each pattern across 5 dimensions:**
#### 1. Frequency Score (0-10)
- **10**: Daily usage (20+ instances or >25% of conversations)
- **8-9**: Multiple times per week (10-20 instances or 15-25%)
- **6-7**: Weekly usage (5-9 instances or 8-15%)
- **4-5**: Bi-weekly to monthly (3-4 instances or 5-8%)
- **2-3**: Monthly or less (2 instances or 3-5%)
- **0-1**: One-off or <3% of conversations
#### 2. Consistency Score (0-10)
- **10**: Identical requirements every time (90-100% similarity)
- **8-9**: Highly consistent with minor variations (75-90%)
- **6-7**: Core structure consistent, details vary (60-75%)
- **4-5**: Recognizable pattern, significant variation (45-60%)
- **2-3**: Loosely related, different each time (30-45%)
- **0-1**: No discernible consistency (<30%)
#### 3. Complexity Score (0-10)
- **10**: Multi-step workflow with decision points, high cognitive load
- **8-9**: Complex methodology requiring expertise/frameworks
- **6-7**: Moderate complexity with structured approach
- **4-5**: Straightforward process with some nuance
- **2-3**: Simple task with minimal steps
- **0-1**: Trivial one-step operation
#### 4. Time Savings Score (0-10)
- **10**: >60 min saved per use (or >10 hours/month total)
- **8-9**: 30-60 min per use (or 5-10 hours/month)
- **6-7**: 15-30 min per use (or 2-5 hours/month)
- **4-5**: 5-15 min per use (or 1-2 hours/month)
- **2-3**: 2-5 min per use (or 30-60 min/month)
- **0-1**: <2 min per use (<30 min/month)
#### 5. Error Reduction Score (0-10)
- **10**: Critical tasks with major error consequences
- **8-9**: Common mistakes significantly impact quality
- **6-7**: Regular pitfalls that skill could prevent
- **4-5**: Occasional errors, modest quality improvement
- **2-3**: Minor inconsistencies, small quality gains
- **0-1**: No error patterns, quality already consistent
#### Composite Scoring
- **Total Score**: Sum of 5 dimensions (0-50 scale)
- **Priority Classification**:
- **Critical** (40-50): Implement immediately
- **High** (30-39): Strong candidates for skill creation
- **Medium** (20-29): Consider for skill creation
- **Low** (10-19): Defer or handle with simple prompts
- **Not Viable** (0-9): Not worth skill automation
### Phase 5: Relationship Mapping & Consolidation
#### A. Overlap Detection
- Identify shared components across patterns
- Map overlapping functionality (>40% shared steps)
- Find hierarchical relationships (high-level task composed of sub-tasks)
- Detect sequential workflows (tasks that occur in sequence)
#### B. Consolidation Strategies
**Use extended reasoning to determine:**
- **Merge** (>60% overlap): Combine into single comprehensive skill
- **Separate with cross-reference** (30-60% overlap): Distinct skills with links
- **Hierarchical**: Main skill + specialized variants → parent/child structure
- **Modular**: Extract common elements → shared templates/references
#### C. Boundary Optimization
Each skill should have:
- **Clear purpose**: Single, well-defined use case
- **Distinct triggers**: Easy to know when to use vs other skills
- **Minimal overlap**: <30% shared functionality with other skills
- **Appropriate scope**: Not too broad (generic) or narrow (over-specialized)
### Phase 6: Prioritization Matrix
Generate 2D matrix visualization:
```
VALUE/IMPACT (High to Low)
│
HIGH │ 🔥 Quick Wins ⭐ Strategic
│ [High-priority [Complex but
│ automation] critical]
│
│ ──────────────────────────────
│
LOW │ 🔧 Automate ⏸️ Defer
│ [Nice-to-have [Not worth
│ efficiency] automating]
│
└────────────────────────────────
LOW FREQUENCY HIGH
```
**Classify each pattern:**
- **X-axis**: Frequency score (0-10)
- **Y-axis**: Average of Complexity, Time Savings, Error Reduction (0-10)
- **Size indicator**: Total composite score
- **Color coding**: Implementation difficulty
**Strategic Recommendations:**
1. **Top 3-5 Quick Wins**: Highest ROI (frequency × impact)
2. **Strategic Skills**: High impact even if lower frequency
3. **Quick Automations**: High frequency, simpler to implement
4. **Defer List**: Patterns not meeting skill-worthiness thresholds
### Phase 7: Skill Package Generation
For each approved skill, create:
#### A. Skill Specification Document
```markdown
## [Skill Name]
**Pattern Evidence:**
- Frequency: [X instances in Y conversations (Z%)]
- Consistency: [X/10 score]
- Time savings: [X hours/month]
**Composite Score: [X/50]**
- Frequency: [X/10]
- Consistency: [X/10]
- Complexity: [X/10]
- Time Savings: [X/10]
- Error Reduction: [X/10]
**Example Conversations:**
1. [Date]: [Brief excerpt showing pattern]
2. [Date]: [Brief excerpt showing pattern]
3. [Date]: [Brief excerpt showing pattern]
**Pattern Components:**
- **Consistent elements**: [What stays the same]
- **Variable elements**: [What changes per instance]
- **Common refinements**: [Typical adjustments user makes]
**Proposed Skill Structure:**
SKILL.md sections:
1. Overview & trigger conditions
2. [Main workflow methodology]
3. Quality standards
4. Examples
Supporting files needed:
- reference.md: [Detailed framework/methodology]
- templates/: [Reusable output templates]
- examples.md: [Additional use cases]
```
#### B. Complete SKILL.md File
Generate production-ready skill with:
- Proper YAML frontmatter (name, description with triggers)
- Clear instructions based on pattern analysis
- Evidence-based examples from actual conversations
- Quality standards derived from user refinement patterns
- Progressive disclosure (link to references for detail)
## Output Formats
After analysis completion, present:
### Summary Report
```markdown
# Workflow Pattern Analysis Report
**Analysis Date**: [Timestamp]
**Conversations Analyzed**: [X conversations across Y time period]
**Patterns Identified**: [X patterns]
**Skills Recommended**: [Y skills]
## 📊 Skill Prioritization Matrix
```mermaid
%%{init: {'theme':'base'}}%%
quadrantChart
title Skill Prioritization: Frequency vs Impact
x-axis Low Frequency --> High Frequency
y-axis Low Impact --> High Impact
quadrant-1 Strategic
quadrant-2 Quick Wins
quadrant-3 Defer
quadrant-4 Automate
[Skill Name 1]: [freq_score/10, impact_score/10]
[Skill Name 2]: [freq_score/10, impact_score/10]
[Skill Name 3]: [freq_score/10, impact_score/10]
```
\```
**Legend:**
- **Quick Wins** (top-right): High frequency, high impact - implement first
- **Strategic** (top-left): Lower frequency but high value - critical capabilities
- **Automate** (bottom-right): High frequency, simpler - nice efficiency gains
- **Defer** (bottom-left): Low priority - consider simple prompts instead
## 🔥 HIGH-PRIORITY OPPORTUNITIES
### 1. [Skill Name]
**Score: [X/50]** (Frequency: X/10, Consistency: X/10, Complexity: X/10, Time: X/10, Error: X/10)
**Pattern Description**: [What you do repeatedly]
**Evidence**:
- Found in [X] conversations ([Y%] of analyzed sample)
- First seen: [Date], Most recent: [Date]
- Average time per instance: [X minutes]
**Example Occurrences**:
1. [Date]: "[Brief excerpt]"
2. [Date]: "[Brief excerpt]"
**Proposed Skill**: "[One-line skill description]"
**Time Savings**: [X hours/month]
---
[Repeat for top 5-8 patterns]
## 💡 MODERATE OPPORTUNITIES
[Briefer summaries of medium-priority patterns]
## 🎯 QUICK AUTOMATION CANDIDATES
[Simple, high-frequency patterns]
## ⏸️ DEFERRED PATTERNS
[Patterns that didn't meet skill-worthiness thresholds]
## 📊 ANALYSIS METADATA
- Total conversations: [X]
- Date range: [earliest] to [latest]
- Unique patterns identified: [X]
- Patterns validated: [Y]
- Cross-pattern overlaps: [Z]
- Recommended consolidations: [N]
```
### Interactive Follow-Up Options
```
What would you like to do next?
A. Generate complete SKILL.md files for [top 3-5 skills]
B. Deep dive into specific pattern: [skill name]
C. Expand analysis with more conversations
D. Focus on specific domain/topic area
E. Adjust scoring weights and recalculate priorities
```
## Quality Standards
### Pattern Validation Requirements
- **Minimum frequency**: 3 instances OR >5% of conversations
- **Consistency threshold**: 70% similarity across instances
- **Evidence requirement**: 2-3 conversation excerpts minimum
- **Time savings minimum**: >30 min/month cumulative
- **Statistical significance**: Validated against sample size
### Skill Recommendations
- **Maximum 8-10 skills**: Focus on highest ROI patterns
- **Clear differentiation**: Each skill has distinct purpose
- **Evidence-based design**: Skill structure reflects actual usage patterns
- **Practical focus**: Prioritize patterns with real time/quality impact
### Analysis Rigor
- **No generic patterns**: Avoid broad categories like "writing" or "analysis"
- **Domain diversity validated**: Patterns should span 6+ distinct topic areas (not just business/coding)
- **Niche specializations identified**: Don't dismiss specialized patterns as outliers
- **No artificial categorization**: Avoid forcing patterns into predefined domains (coding, writing, business, analysis)
- **Validated frequencies**: Ensure counts are accurate within sample
- **Temporal awareness**: Note if pattern is emerging, stable, or declining
- **User context**: Consider user's domain expertise and role
## Examples
### Example 1: Quick Scan Request
**User**: "Analyze my recent chats for automation opportunities"
**Claude**:
I'll perform a quick scan of your recent conversation history to identify skill opportunities.
[Uses recent_chats(n=30) multiple times]
📊 **Quick Scan Results** (30 conversations analyzed)
🔥 **TOP OPPORTUNITY**:
**"Email Response Composer"** - Score: 42/50
- Found in 9 conversations (30%)
- Pattern: You regularly ask me to draft professional emails with specific tone and structure requirements
- Time savings: ~90 min/month (10 min per email × 9 emails)
- Evidence: Consistent 3-paragraph structure with greeting, main content, action items
Want me to:
A. Generate complete skill package for this
B. Continue analysis for more patterns (expand to 50-75 conversations)
C. Focus on other domains
### Example 2: Targeted Domain Analysis
**User**: "Find patterns in how I use you for coding tasks"
**Claude**:
I'll analyze your coding-related conversations to identify workflow patterns.
[Uses conversation_search for coding terms + recent_chats filtering]
📊 **Coding Workflow Analysis** (18 relevant conversations)
🔥 **TOP PATTERNS**:
1. **"Code Review Checklist Generator"** - Score: 38/50
- Found in 7 code review discussions
- Pattern: You ask for language-specific review checklists before reviewing PRs
- Consistency: 85% (always includes security, performance, style sections)
2. **"API Documentation Humanizer"** - Score: 35/50
- Found in 5 documentation tasks
- Pattern: Converting technical API specs into developer-friendly guides
- Structure: Always includes quick start, common use cases, troubleshooting
Want me to build skills for these patterns?
### Example 3: Deep Dive Analysis
**User**: "Do a comprehensive analysis of my workflow over the last few months"
**Claude**:
I'll perform a deep analysis of your conversation history. This will take 10-15 minutes to ensure comprehensive coverage.
[Uses recent_chats extensively, conversation_search for discovered topics, temporal sampling]
📊 **Comprehensive Workflow Analysis**
- **Conversations analyzed**: 120
- **Date range**: [3 months]
- **Patterns identified**: 15
- **Skills recommended**: 6
🔥 **CRITICAL PRIORITY** (Score 40-50):
1. **"Weekly Status Report Generator"** - Score: 47/50
- Frequency: 12 instances (10% of conversations)
- Consistency: 95% - always same structure
- Evidence: Every Monday, you format updates in identical 5-section template
- Time savings: 240 min/month (20 min/week × 4 weeks × 3 months avg)
⭐ **HIGH PRIORITY** (Score 30-39):
2. **"Client Proposal Framework"** - Score: 36/50
3. **"Meeting Notes Structurer"** - Score: 34/50
4. **"Technical Concept Explainer"** - Score: 31/50
[Full analysis report with evidence, prioritization matrix, skill specifications]
**Recommended Implementation Path**:
1. Start with "Weekly Status Report Generator" (highest ROI)
2. Build "Client Proposal Framework" and "Meeting Notes Structurer" next (complementary workflows)
3. Evaluate remaining patterns after 2-4 weeks of usage
Generate complete skill packages now? [Y/N]
## Anti-Patterns to Avoid
**Don't recommend skills for:**
- **One-off variations**: Tasks that seem similar but are fundamentally different each time
- **Over-simplified tasks**: Things easier to just ask directly than invoke a skill
- **Better solved by tools**: When external apps/services do it better
- **Insufficient data**: Patterns with <3 instances or <5% frequency (unless strategic)
- **Generic categories**: Broad skills like "help with writing" or "analyze data"
**Red flags in patterns:**
- High frequency but no consistency (chaotic variation)
- High consistency but very low frequency (use a prompt template instead)
- Pattern is declining over time (user may have found better solution)
- Task requires real-time data or external authentication (needs MCP, not skill)
## When to Use This Skill
**✅ Use this skill when:**
- User requests analysis of their conversation patterns
- User wants to identify automation opportunities
- User asks what skills they should create
- User mentions repetitive tasks or workflows
- User wants evidence-based skill recommendations
- User is in web interface (can't use export-based analysis)
**❌ Don't use this skill when:**
- User has conversation export files available (use export-based plugin instead for more comprehensive analysis)
- User wants cross-platform ChatGPT + Claude analysis (requires exports)
- User has very few conversations (<10) making pattern detection unreliable
- User wants to build specific skill they already have in mind
- User is asking about existing skills or community skills
**⚡ Proactive Use:**
When you detect potential patterns during normal conversation, offer:
```
💭 Pattern detected: This is the [Xth] time you've asked me to [action].
Would you like me to analyze your conversation history for similar
patterns and recommend a Custom Skill? I can identify other automation
opportunities you might not have noticed.
[Yes, analyze] [Not now]
```
## Progressive Disclosure Strategy
**Keep main analysis concise by organizing information hierarchically:**
1. **Quick overview first**: Summary report with top 3-5 opportunities
2. **Details on demand**: Expand specific patterns when user shows interest
3. **Implementation when ready**: Generate complete skill packages only after user approval
4. **Iterative refinement**: Allow user to adjust scoring weights, focus areas, or analysis depth
**Load additional detail only when:**
- User requests deep dive on specific pattern
- Generating complete skill packages (not just analysis)
- User wants to understand scoring methodology in detail
- Building skills for complex domains requiring extensive examples

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# 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
```markdown
# 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](https://github.com/hirefrank/hirefrank-marketplace/issues)
- Discuss improvements: [GitHub Discussions](https://github.com/hirefrank/hirefrank-marketplace/discussions)
## License
MIT License - see repository root for details
---
**Built to bridge web accessibility with export-quality analysis rigor**

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---
name: workflow-pattern-analyzer
description: Analyzes recent conversation history using chat tools to identify recurring workflow patterns and generate Custom Skills recommendations with statistical rigor. Use when users request workflow analysis, pattern identification, skill generation suggestions, or automation opportunities based on their AI usage patterns without requiring conversation exports.
---
# Workflow Pattern Analyzer
## Instructions
This skill provides comprehensive conversation pattern analysis using Claude's native chat history tools (`recent_chats` and `conversation_search`) to identify skill-worthy automation opportunities with the statistical rigor of export-based analysis.
**Core Capabilities:**
- Web interface compatible (no exports required)
- Statistical pattern validation and scoring
- Frequency analysis and temporal tracking
- Evidence-based skill recommendations
- Complete skill package generation
## Analysis Framework
This skill uses the **[shared analysis methodology](../../shared/analysis-methodology.md)** with tool-based data collection adaptations.
### Phase 1: Data Collection Strategy
**Determine Analysis Scope:**
Ask user: "How deep should I analyze your conversation history?"
**Options:**
- **Quick Scan** (20-30 conversations, ~2-3 min): Recent patterns and immediate opportunities
- **Standard Analysis** (50-75 conversations, ~5-7 min): Comprehensive pattern detection
- **Deep Dive** (100+ conversations, ~10-15 min): Full workflow mapping with temporal trends
- **Targeted Search** (variable): Focus on specific topics or time periods
**Data Collection Process:**
1. **Broad Sampling**: Use `recent_chats(n=30)` multiple times with varied parameters to get diverse coverage
2. **Temporal Distribution**: Sample conversations across different time periods (recent, 1 week ago, 1 month ago)
3. **Topic Exploration**: Use `conversation_search` for domains mentioned by user or detected in initial sampling
4. **Depth vs Breadth**: Balance comprehensive coverage with processing efficiency
### Phase 2-6: Core Analysis
Apply the **[shared analysis methodology](../../shared/analysis-methodology.md)** phases:
- **Phase 2**: Pattern Discovery & Classification (explicit, implicit, domain, temporal)
- **Phase 3**: Frequency Analysis & Validation (occurrence metrics, statistical validation)
- **Phase 4**: Skill-Worthiness Scoring (0-50 composite scale across 5 dimensions)
- **Phase 5**: Relationship Mapping & Consolidation (overlap detection, boundary optimization)
- **Phase 6**: Prioritization Matrix & Recommendations (frequency vs impact visualization)
See [shared methodology](../../shared/analysis-methodology.md) for complete scoring rubrics and quality standards.
### Phase 7: Skill Package Generation
**For each approved skill, create:**
**A. Skill Specification Document:**
```markdown
## [Skill Name]
**Pattern Evidence:**
- Frequency: [X instances in Y conversations (Z%)]
- Consistency: [X/10 score]
- Time savings: [X hours/month]
**Composite Score: [X/50]**
- Frequency: [X/10]
- Consistency: [X/10]
- Complexity: [X/10]
- Time Savings: [X/10]
- Error Reduction: [X/10]
**Example Conversations:**
1. [Date]: [Brief excerpt showing pattern]
2. [Date]: [Brief excerpt showing pattern]
3. [Date]: [Brief excerpt showing pattern]
**Pattern Components:**
- **Consistent elements**: [What stays the same]
- **Variable elements**: [What changes per instance]
- **Common refinements**: [Typical adjustments user makes]
**Proposed Skill Structure:**
SKILL.md sections:
1. Overview & trigger conditions
2. [Main workflow methodology]
3. Quality standards
4. Examples
Supporting files needed:
- reference.md: [Detailed framework/methodology]
- templates/: [Reusable output templates]
- examples.md: [Additional use cases]
```
**B. Complete SKILL.md File:**
Generate production-ready skill with:
- Proper YAML frontmatter (name, description with triggers)
- Clear instructions based on pattern analysis
- Evidence-based examples from actual conversations
- Quality standards derived from user refinement patterns
- Progressive disclosure (link to references for detail)
## Output Formats
**After analysis completion, present:**
### Summary Report
```markdown
# Workflow Pattern Analysis Report
**Analysis Date**: [Timestamp]
**Conversations Analyzed**: [X conversations across Y time period]
**Patterns Identified**: [X patterns]
**Skills Recommended**: [Y skills]
## 🔥 HIGH-PRIORITY OPPORTUNITIES
### 1. [Skill Name]
**Score: [X/50]** (Frequency: X/10, Consistency: X/10, Complexity: X/10, Time: X/10, Error: X/10)
**Pattern Description**: [What you do repeatedly]
**Evidence**:
- Found in [X] conversations ([Y%] of analyzed sample)
- First seen: [Date], Most recent: [Date]
- Average time per instance: [X minutes]
**Example Occurrences**:
1. [Date]: "[Brief excerpt]"
2. [Date]: "[Brief excerpt]"
**Proposed Skill**: "[One-line skill description]"
**Time Savings**: [X hours/month]
---
[Repeat for top 5-8 patterns]
## 💡 MODERATE OPPORTUNITIES
[Briefer summaries of medium-priority patterns]
## 🎯 QUICK AUTOMATION CANDIDATES
[Simple, high-frequency patterns]
## ⏸️ DEFERRED PATTERNS
[Patterns that didn't meet skill-worthiness thresholds]
## 📊 ANALYSIS METADATA
- Total conversations: [X]
- Date range: [earliest] to [latest]
- Unique patterns identified: [X]
- Patterns validated: [Y]
- Cross-pattern overlaps: [Z]
- Recommended consolidations: [N]
```
### Interactive Follow-Up Options
```
What would you like to do next?
A. Generate complete SKILL.md files for [top 3-5 skills]
B. Deep dive into specific pattern: [skill name]
C. Expand analysis with more conversations
D. Focus on specific domain/topic area
E. Adjust scoring weights and recalculate priorities
```
## Quality Standards
All quality standards follow the **[shared analysis methodology](../../shared/analysis-methodology.md#quality-standards)**:
- Pattern validation requirements (frequency, consistency, evidence)
- Skill consolidation rules (max 8-12 skills, clear boundaries)
- Skill package generation standards
- Anti-patterns to avoid
## Progressive Disclosure Strategy
**Keep this SKILL.md concise by referencing:**
- **Core methodology**: [shared/analysis-methodology.md](../../shared/analysis-methodology.md)
- **Detailed scoring rubrics**: See methodology Phase 4
- **Quality standards**: See methodology Quality Standards section
- **Anti-patterns**: See methodology Anti-Patterns section
**Load additional context only when:**
- User requests deep dive on specific pattern
- Generating complete skill packages (not just analysis)
- User wants to understand scoring methodology in detail
- Building reference materials for complex domains
## Examples
### Example 1: Quick Scan Request
**User**: "Analyze my recent chats for automation opportunities"
**Claude**:
I'll perform a quick scan of your recent conversation history to identify skill opportunities.
[Uses recent_chats(n=30) multiple times]
📊 **Quick Scan Results** (30 conversations analyzed)
🔥 **TOP OPPORTUNITY**:
**"Email Response Composer"** - Score: 42/50
- Found in 9 conversations (30%)
- Pattern: You regularly ask me to draft professional emails with specific tone and structure requirements
- Time savings: ~90 min/month (10 min per email × 9 emails)
- Evidence: Consistent 3-paragraph structure with greeting, main content, action items
Want me to:
A. Generate complete skill package for this
B. Continue analysis for more patterns (expand to 50-75 conversations)
C. Focus on other domains
### Example 2: Targeted Domain Analysis
**User**: "Find patterns in how I use you for coding tasks"
**Claude**:
I'll analyze your coding-related conversations to identify workflow patterns.
[Uses conversation_search for coding terms + recent_chats filtering]
📊 **Coding Workflow Analysis** (18 relevant conversations)
🔥 **TOP PATTERNS**:
1. **"Code Review Checklist Generator"** - Score: 38/50
- Found in 7 code review discussions
- Pattern: You ask for language-specific review checklists before reviewing PRs
- Consistency: 85% (always includes security, performance, style sections)
2. **"API Documentation Humanizer"** - Score: 35/50
- Found in 5 documentation tasks
- Pattern: Converting technical API specs into developer-friendly guides
- Structure: Always includes quick start, common use cases, troubleshooting
Want me to build skills for these patterns?
### Example 3: Deep Dive Analysis
**User**: "Do a comprehensive analysis of my workflow over the last few months"
**Claude**:
I'll perform a deep analysis of your conversation history. This will take 10-15 minutes to ensure comprehensive coverage.
[Uses recent_chats extensively, conversation_search for discovered topics, temporal sampling]
📊 **Comprehensive Workflow Analysis**
- **Conversations analyzed**: 120
- **Date range**: [3 months]
- **Patterns identified**: 15
- **Skills recommended**: 6
🔥 **CRITICAL PRIORITY** (Score 40-50):
1. **"Weekly Status Report Generator"** - Score: 47/50
- Frequency: 12 instances (10% of conversations)
- Consistency: 95% - always same structure
- Evidence: Every Monday, you format updates in identical 5-section template
- Time savings: 240 min/month (20 min/week × 4 weeks × 3 months avg)
⭐ **HIGH PRIORITY** (Score 30-39):
2. **"Client Proposal Framework"** - Score: 36/50
3. **"Meeting Notes Structurer"** - Score: 34/50
4. **"Technical Concept Explainer"** - Score: 31/50
[Full analysis report with evidence, prioritization matrix, skill specifications]
**Recommended Implementation Path**:
1. Start with "Weekly Status Report Generator" (highest ROI)
2. Build "Client Proposal Framework" and "Meeting Notes Structurer" next (complementary workflows)
3. Evaluate remaining patterns after 2-4 weeks of usage
Generate complete skill packages now? [Y/N]
## When to Use This Skill
**✅ Use this skill when:**
- User requests analysis of their conversation patterns
- User wants to identify automation opportunities
- User asks what skills they should create
- User mentions repetitive tasks or workflows
- User wants evidence-based skill recommendations
- User is in web interface (can't use export-based analysis)
**❌ Don't use this skill when:**
- User has conversation export files available (use export-based plugin instead for more comprehensive analysis)
- User wants cross-platform ChatGPT + Claude analysis (requires exports)
- User has very few conversations (<10) making pattern detection unreliable
- User wants to build specific skill they already have in mind
- User is asking about existing skills or community skills
**⚡ Proactive Use:**
When you detect potential patterns during normal conversation, offer:
```
💭 Pattern detected: This is the [Xth] time you've asked me to [action].
Would you like me to analyze your conversation history for similar
patterns and recommend a Custom Skill? I can identify other automation
opportunities you might not have noticed.
[Yes, analyze] [Not now]
```
## Anti-Patterns to Avoid
See **[shared methodology anti-patterns](../../shared/analysis-methodology.md#anti-patterns-to-avoid)** for complete guidance on:
- Tasks not suitable for skills
- Red flags in patterns
- When to use MCP vs skills
- Common recommendation pitfalls