conversation-analyzer
Analyzes your Claude Code conversation history to identify patterns, common mistakes, and opportunities for workflow improvement.
Skill by mhattingpete
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/mhattingpete-conversation-analyzer
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: productivity, agent-building
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/conversation-analyzer- Your home directory (~)
What's inside
- Skill name
conversation-analyzer- Description
- 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.
- License
- Apache-2.0
- Instructions
- 126 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (2)
- SKILL.md
- analyze_history.py · scripts/
SKILL.md (first lines)
# 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:** ```bash 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
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Signals
- Stars
- 688
- Forks
- 98
- Open issues
- 4
- Last commit (this folder)
- Nov 10, 2025 (11 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Create HTML dashboards with KPI metric cards, bar/pie/line charts, progress indicators, and data visualizations.
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Break down feature requests into detailed, implementable plans with clear tasks.
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Create HTML flowcharts and process diagrams with decision trees, color-coded stages, arrows, and swimlanes.
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Cite this page
conversation-analyzer skill. Agent Mart. https://agentmart.dev/skills/mhattingpete-conversation-analyzer (last updated 2026-10-24)