Skip to main content Analyzes your Claude Code conversation history to identify patterns, common mistakes, and opportunities for workflow improvement. Use when user wants to understand usage patterns, optimize workflow, identify automation opportunities, or check if they're following best practices.
npx skills add mhattingpete/claude-skills-marketplace --skill conversation-analyzer ai-agents anthropic automation claude-code claude-skills developer-tools
Conversation Analyzer
Analyzes your Claude Code conversation history to identify patterns, common mistakes, and workflow improvement opportunities.
When to Use
"analyze my conversations"
"review my Claude Code history"
"what patterns do you see in my usage"
"how can I improve my workflow"
"am I using Claude Code effectively"
What It Analyzes
Request type distribution (bug fixes, features, refactoring, queries, testing)Most active projects Common error keywords Time-of-day patterns Repetitive tasks (automation opportunities)Vague requests causing back-and-forthComplex tasks attempted without planningRecurring bugs/errors
Analysis Scope
Default: Last 200 conversations for recency and relevance.
Methodology
1. Request Type Distribution
Categorizes by: bug fixes, feature additions, refactoring, information queries, testing, other.
2. Project Activity
Tracks which projects consume most time, identifies project-specific patterns.
3. Time Patterns
Hour-of-day usage distribution, identifies peak productivity times.
4. Common Mistakes
Vague requests : Initial requests lacking context vs. acceptable follow-ups
Repeated fixes : Same issues occurring multiple times
Complex tasks : Multi-step requests without planning
Repetitive commands : Manual tasks that could be automated
5. Error Analysis Frequency of error-related requests, common error keywords, recurring problems.
6. Automation Opportunities Identifies repeated exact requests, suggests skills, slash commands, or scripts.
Output
Statistics : Request types, active projects, timing patternsPatterns : Common tasks, repetitive commands, complexity indicatorsIssues : Specific problems with examplesRecommendations : Prioritized, actionable improvements
Tools Used
Read : Load history file (~/.claude/history.jsonl)Write : Create analysis reports if requestedBash : Execute Python analysis scriptDirect analysis : Parse JSON programmatically
Analysis Script Uses scripts/analyze_history.py for comprehensive analysis:
Loads and parses ~/.claude/history.jsonl
Analyzes patterns across multiple dimensions
Identifies common mistakes and inefficiencies
Generates actionable recommendations
Outputs detailed reports
Usage within skill:
Runs automatically when user requests analysis.
cd ~/.claude/plugins/*/productivity-skills/conversation-analyzer/scripts
python3 analyze_history.py
conversation_analysis.txt - Detailed pattern analysisrecommendations.txt - Specific improvement suggestions
Example Output Analyzed last 200 conversations:
- 60% general tasks, 15% bug fixes, 13% feature additions
- Project "ultramerge" dominates 58% of activity
- Same test-fixing request made 8 times
- 19 multi-step requests without planning
- Peak productivity: 13:00-15:00
Recommendations:
- Use test-fixing skill for recurring test failures
- Create project-specific utilities for ultramerge
- Use feature-planning skill for complex requests
- Add tests to prevent recurring bugs
- Schedule complex work during peak hours
Success Criteria
User understands usage patterns
Concrete, actionable recommendations
Specific examples from history
Prioritized by impact (quick wins vs long-term)
User can immediately apply improvements
Integration
feature-planning : Implement recommended improvementstest-fixing : Address recurring test failuresgit-pushing : Commit workflow improvements
Privacy Note All analysis happens locally. Conversation history never leaves user's machine.
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