cc-insights is a specialized productivity tool designed to unlock the hidden value within Claude Code conversation histories. By implementing RAG-powered semantic search and automated pattern detection, it transforms raw interaction logs into actionable intelligence, allowing developers to analyze coding patterns, track activity trends, and retrieve past solutions through natural language.

When should I use cc-insights?

cc-insights is useful in the following scenarios: • Semantic Knowledge Retrieval: Find specific technical solutions, bug fixes, or architectural decisions from past conversations using meaning-based search rather than just keywords. • Development Pattern Analysis: Generate automated insight reports to identify 'file hotspots' and recurring topics, helping developers understand which parts of the codebase require the most attention. • Activity & Progress Reporting: Create comprehensive reports on tool usage and development trends to visualize project velocity and individual contribution patterns over time. • Interactive Workflow Visualization: Launch a dedicated Next.js dashboard to explore conversation metadata and activity metrics through an intuitive web interface.

name	cc-insights
version	0.2.1
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

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

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

When & Why to Use This Skill

Use Cases