| name | cc-insights |
| description | Use PROACTIVELY when searching past Claude Code conversations, analyzing development patterns, or generating activity reports. Automatically processes conversation history from the project, enables RAG-powered semantic search, and generates insight reports with pattern detection. Provides optional dashboard for visualization. Not for real-time analysis or cross-project searches. |
Claude Code Insights
Unlock the hidden value in your Claude Code conversation history through automatic processing, semantic search, and intelligent insight generation.
Overview
This skill automatically analyzes your project's Claude Code conversations (stored in ~/.claude/projects/[project]/*.jsonl) to provide:
- RAG-Powered Semantic Search: Find conversations by meaning, not just keywords
- Automatic Insight Reports: Pattern detection, file hotspots, tool usage analytics
- Activity Trends: Understand your development patterns over time
- Knowledge Extraction: Surface recurring topics, solutions, and best practices
- Zero Manual Effort: Fully automatic processing of existing conversations
When to Use This Skill
Trigger Phrases:
- "Find conversations about [topic]"
- "Generate weekly insights report"
- "What files do I modify most often?"
- "Launch the insights dashboard"
- "Export insights as [format]"
Use Cases:
- Search past conversations by topic or file
- Generate activity reports and insights
- Understand development patterns over time
- Extract knowledge and recurring solutions
- Visualize activity with interactive dashboard
NOT for:
- Real-time conversation analysis (analyzes history only)
- Conversations from other projects (project-specific)
- Manual conversation logging (automatic only)
Response Style
Informative and Visual: Present search results with relevance scores and snippets. Generate reports with clear metrics and ASCII visualizations. Offer to save or export results.
Mode Selection
| User Request | Mode | Reference |
|---|
| "Find conversations about X" | Search | modes/mode-1-search.md |
| "Generate insights report" | Insights | modes/mode-2-insights.md |
| "Launch dashboard" | Dashboard | modes/mode-3-dashboard.md |
| "Export as JSON/CSV/HTML" | Export | modes/mode-4-export.md |
Mode Overview
Mode 1: Search Conversations
Find past conversations using semantic search (by meaning) or metadata search (by files/tools).
→ Details: modes/mode-1-search.md
Mode 2: Generate Insights
Analyze patterns and generate reports with file hotspots, tool usage, and knowledge highlights.
→ Details: modes/mode-2-insights.md
Mode 3: Interactive Dashboard
Launch a Next.js web dashboard for rich visualization and exploration.
→ Details: modes/mode-3-dashboard.md
Mode 4: Export and Integration
Export insights as Markdown, JSON, CSV, or HTML for sharing and integration.
→ Details: modes/mode-4-export.md
Initial Setup
First time usage:
- Install dependencies:
pip install -r requirements.txt
- Run initial processing (automatic on first use)
- Build embeddings (one-time, ~1-2 min)
- Ready to search and analyze!
What happens automatically:
- Scans
~/.claude/projects/[current-project]/*.jsonl
- Extracts and indexes conversation metadata
- Builds vector embeddings for semantic search
- Creates SQLite database for fast queries
Important Reminders
- Automatic processing: Skill updates index on each use (incremental)
- First run is slow: Embedding creation takes 1-2 minutes
- Project-specific: Analyzes only current project's conversations
- Dashboard requires Node.js: v18+ for the Next.js dashboard
- ChromaDB for search: Vector similarity search for semantic queries
Limitations
- Only analyzes JSONL conversation files from Claude Code
- Requires sentence-transformers for embedding creation
- Dashboard is local only (localhost:3000)
- Large conversation histories may take longer to process initially
Reference Materials
| Resource | Purpose |
|---|
modes/*.md | Detailed mode instructions |
reference/troubleshooting.md | Common issues and fixes |
scripts/ | Processing and indexing scripts |
dashboard/ | Next.js dashboard application |
Success Criteria
Tech Stack: Python (processing), SQLite (metadata), ChromaDB (vectors), Next.js (dashboard)
