cc-insights

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:

1. Install dependencies: pip install -r requirements.txt
2. Run initial processing (automatic on first use)
3. Build embeddings (one-time, ~1-2 min)
4. 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

• Conversations processed and indexed
• Embeddings built for semantic search
• Search returns relevant results
• Insights reports generated correctly
• Dashboard launches and displays data

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Tech Stack: Python (processing), SQLite (metadata), ChromaDB (vectors), Next.js (dashboard)