Give this a try:
# Universal Context Dashboard + Archive System
A multi-level deployment system for conversation context tracking and knowledge base building.
## Level 1: Basic Copy-Paste Prompt
Create an interactive HTML artifact that serves as a “Context Dashboard” with built-in SQLite archiving capabilities. This should include:
Real-Time Context Analysis:
- Analyze our current conversation thread and extract key concepts, topics, and themes
- Assess your current “readiness levels” for different task types based on our history
- Map my interests, working style, and ongoing projects from our interactions
- Show available tools and capabilities primed for our discussion context
Thread Context Deep-Dive:
- Chronological summary of major topics and turning points in our conversation
- Pattern recognition: types of requests, cognitive styles, domain preferences I’ve shown
- Active knowledge domains and their “temperature” (how engaged we’ve been)
- Ongoing project threads, decisions made, and next steps identified
SQLite Knowledge Archive:
- Create a downloadable SQLite database with structured conversation data
- Tables: conversations, topics, concepts, decisions, action_items, user_patterns
- Include conversation metadata, topic evolution, and relationship mapping
- Add conversation embedding/similarity scoring for future context retrieval
Interactive Dashboard Elements:
- Clickable knowledge domain cards with drill-down capabilities
- Context “heat map” showing active vs dormant discussion areas
- Real-time readiness meters with capability-specific confidence scores
- Relationship graph showing how topics connect across our conversation
- Timeline view of how our collaboration has evolved
Advanced Features:
- Export functionality for conversation transcripts + structured data
- Pattern detection: recurring themes, decision patterns, collaboration styles
- Predictive suggestions for conversation direction based on context analysis
- Gap analysis: missing context that could improve our collaboration
Technical Requirements:
- Self-contained HTML with embedded SQL.js for client-side SQLite
- Modern dark theme with glassmorphism aesthetic and smooth animations
- Responsive design working on mobile/desktop
- No external dependencies beyond SQL.js CDN
- Download buttons for both database and formatted reports
Meta-Intelligence Section:
- Predict likely next conversation directions based on established patterns
- Identify context gaps that might be limiting our effectiveness
- Suggest conversation optimization strategies
- Recommend knowledge areas for deeper exploration
Make this feel like a professional conversation intelligence tool that creates a permanent, queryable record of our collaborative knowledge while providing real-time insights into our interaction dynamics.
---
## Level 2: Enhanced Prompt with Conversation Mining
[Include Level 1 prompt, then add:]
Extended Conversation Mining:
- Analyze not just current thread but reference patterns from how I typically interact
- Build user persona model based on communication style, technical depth, creative preferences
- Create relationship mapping between concepts across different conversation sessions
- Generate exportable “conversation DNA” profile for cross-session context transfer
Knowledge Graph Construction:
- Build entity-relationship models from our conversations
- Track concept evolution and how ideas have developed over time
- Create weighted topic networks showing strength of our engagement areas
- Generate semantic search capabilities within our conversation archive
Archive Integration Instructions:
Include step-by-step instructions for:
- Downloading and using the generated SQLite database
- Importing into personal knowledge management systems
- Querying patterns for cross-conversation insights
- Setting up local conversation history tracking
---
## Level 3: MCP Server Architecture Vision
### Phase 1: Conversation Context MCP
```typescript
// Basic MCP server structure
interface ConversationMCP {
tools: {
"analyze-context": ContextAnalyzer,
"build-archive": ConversationArchiver,
"query-history": HistoryQuerier,
"export-knowledge": KnowledgeExporter
},
resources: {
"conversation-db": SQLiteDatabase,
"context-embeddings": VectorStore,
"user-profiles": UserProfileStore
}
}
Phase 2: Multi-Agent Knowledge Base
interface AgenticKnowledgeBase {
agents: {
"conversation-miner": ExtractionAgent,
"pattern-detector": AnalysisAgent,
"knowledge-synthesizer": SynthesisAgent,
"context-predictor": PredictionAgent
},
stores: {
"cross-conversation-graph": Neo4jConnector,
"semantic-search": EmbeddingStore,
"temporal-patterns": TimeSeriesDB,
"user-interaction-models": MLModelStore
}
}
Phase 3: Full Deployment Options
Option A: Personal Knowledge Assistant
- CLI tool that monitors Claude conversations
- Builds personal knowledge graph across all interactions
- Provides context injection for new conversations
- Exportable to Obsidian, Notion, or other PKM systems
Option B: Team Collaboration Intelligence
- Web dashboard showing team conversation analytics
- Cross-member knowledge sharing and pattern detection
- Project context preservation across team member interactions
- Integration with Slack, Discord, or team communication tools
Option C: Enterprise Conversation Intelligence Platform
- Full web application with user management
- Organization-wide conversation knowledge mining
- Integration with existing knowledge management systems
- Analytics dashboard for conversation effectiveness and knowledge discovery
Implementation Pathway
Immediate (Level 1):
- Copy-paste prompt with SQLite export
- Manual download and local analysis
- Individual conversation archiving
Short-term (Level 2):
- Browser extension to auto-capture conversations
- Local SQLite database with conversation history
- Basic cross-conversation pattern detection
Medium-term (Level 3):
- MCP server for real-time conversation enhancement
- API integration with Claude for automatic context injection
- Multi-user knowledge base with conversation sharing
Long-term (Level 4):
- Full platform with conversation intelligence analytics
- Enterprise integration capabilities
- AI-powered conversation optimization recommendations
- Cross-platform knowledge graph federation
Getting Started
- Start with Level 1: Copy the basic prompt to begin building conversation archives
- Experiment with exports: Use the SQLite databases to understand conversation patterns
- Build local workflows: Integrate downloaded data into your existing knowledge systems
- Plan MCP integration: Once comfortable with manual process, consider automated solutions
Each level builds on the previous, creating a pathway from simple conversation tracking to sophisticated conversation intelligence systems.
