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

1. Request type distribution (bug fixes, features, refactoring, queries, testing)
2. Most active projects
3. Common error keywords
4. Time-of-day patterns
5. Repetitive tasks (automation opportunities)
6. Vague requests causing back-and-forth
7. Complex tasks attempted without planning
8. Recurring 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

Structured report with:

• Statistics: Request types, active projects, timing patterns
• Patterns: Common tasks, repetitive commands, complexity indicators
• Issues: Specific problems with examples
• Recommendations: Prioritized, actionable improvements

Tools Used

• Read: Load history file (

  ~/.claude/history.jsonl

  )
• Write: Create analysis reports if requested
• Bash: Execute Python analysis script
• Direct analysis: Parse JSON programmatically

Analysis Script

Uses

scripts/analyze_history.py

for comprehensive analysis:

Capabilities:

• 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.

Standalone usage:

cd ~/.claude/plugins/*/productivity-skills/conversation-analyzer/scripts
python3 analyze_history.py

Outputs:

• conversation_analysis.txt

  - Detailed pattern analysis

• recommendations.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 improvements
• test-fixing: Address recurring test failures
• git-pushing: Commit workflow improvements

Privacy Note

All analysis happens locally. Conversation history never leaves user's machine.