Computer Operations

The Computer plugin provides a Star Trek LCARS-themed interface for voice transcription, AI analysis, chart generation, web search, monitoring, logging, comparison, summarization, translation, explanation, knowledge management, workflow pipelines, and export/reports.

Server Management

• Start: /computer:computer or bash "${CLAUDE_PLUGIN_ROOT}/scripts/start.sh"
• Stop: /computer:computer stop
• Status: /computer:status
• Port: 3141
• URL: http://localhost:3141

API Endpoints (push data to the UI)

All POST endpoints broadcast to connected WebSocket clients for real-time display.

Method	Path	Purpose
GET	/api/health	Server health check
GET/POST	/api/transcripts	Store + display transcripts
GET/POST	/api/analyses	Store + display analyses (+ desktop notification)
POST	/api/charts	Render Chart.js visualization
POST	/api/search-results	Display search results
GET/POST	/api/logs	Captain's log entries
GET/POST	/api/monitors	Monitor status tracking (+ alert notifications)
GET/POST	/api/comparisons	Side-by-side comparisons
GET/POST	/api/knowledge	Knowledge base entries (LanceDB vector storage)
POST	/api/knowledge/search	Semantic search with method selection
DELETE	/api/knowledge/:id	Remove knowledge entry and all chunks
POST	/api/knowledge/bulk	Bulk ingest multiple documents
GET	/api/knowledge/stats	Knowledge base statistics
POST	/api/tts/speak	Generate spoken response
POST	/api/claude/query	Stream Claude response (SSE)
POST	/api/transcribe/file	Upload audio for Whisper transcription

Commands

Command	Purpose
/computer:computer	Launch/stop server
/computer:analyze	Sentiment, topics, entities, action items
/computer:search	Web search with synthesis
/computer:transcribe	Whisper audio transcription
/computer:status	System diagnostics
/computer:compare	Side-by-side comparison of files/text
/computer:summarize	Multi-level document summarization
/computer:monitor	Set up watches on URLs/files/processes
/computer:log	Captain's log entries
/computer:brief	Activity briefing and status report
/computer:pipeline	Chain multiple operations in sequence
/computer:know	Store, retrieve, and search knowledge base
/computer:export	Generate formatted reports (markdown/html/json)

Agents

Agent	Model	Purpose
analyst	Opus	Sentiment, topics, action items, summaries
researcher	Sonnet	Web search and information synthesis
visualizer	Sonnet	Chart.js config generation with LCARS colors
transcription-processor	Sonnet	Transcript cleanup and structuring
comparator	Opus	Side-by-side comparison with radar charts
summarizer	Opus	Multi-level summarization (executive → detailed)
monitor	Sonnet	Continuous monitoring and alerting
translator	Sonnet	Multi-language translation with cultural context
explainer	Opus	Layered explanations (ELI5 → deep dive)
pipeline	Opus	Workflow orchestration chaining operations
knowledge	Opus	Persistent knowledge store, retrieve, synthesize

UI Panels

Panel	Purpose
Dashboard	Bridge console — system stats, active monitors, recent logs, activity feed
Main	Conversation with Claude
Transcript	Live voice transcription + file upload
Analysis	Sentiment, topic, entity analysis results
Charts	Chart.js visualizations
Search	Web search results
Log	Captain's log entries with stardates
Monitor	Active monitor cards with status and history
Compare	Side-by-side comparative analysis
Knowledge	Searchable knowledge base with confidence levels and tags

Desktop Notifications

macOS desktop notifications fire automatically for:

• Analysis complete: When a new analysis is posted
• Monitor alerts: When a monitor status is "alert" or "triggered"
• Monitor updates: General notification for monitor status changes

Smart Voice Routing

When the user speaks via microphone, the input is analyzed for command intent:

• "Computer, analyze [text]" → auto-routes to /computer:analyze
• "Computer, search [query]" → auto-routes to /computer:search
• "Computer, compare [items]" → auto-routes to /computer:compare
• "Computer, summarize [text]" → auto-routes to /computer:summarize
• "Computer, monitor [target]" → auto-routes to /computer:monitor
• "Computer, translate [text]" → auto-routes to /computer:translate
• "Computer, explain [topic]" → auto-routes to /computer:explain
• "Captain's log [entry]" → auto-routes to /computer:log
• "Computer, remember [fact]" → auto-routes to /computer:know
• "Computer, status" → auto-routes to /computer:status
• "Computer, briefing" → auto-routes to /computer:brief

A green "Routed" badge appears in the UI when voice routing is active.

LCARS Chart Colors

Primary: #FF9900, #CC99CC, #9999FF, #FF9966, #CC6699, #99CCFF, #FFCC00 Background: #000000, Text: #FF9900, Grid: #333333

Data Storage

JSON files in ${CLAUDE_PLUGIN_ROOT}/data/:

• transcripts/ — Voice and file transcripts
• analyses/ — Analysis results, summaries, explanations, translations
• sessions/ — Conversation logs
• logs/ — Captain's log entries
• monitors/ — Monitor configurations and status
• comparisons/ — Comparison results
• vectordb/ — LanceDB vector database (knowledge embeddings, chunks, metadata)

Cross-Referencing Prior Results

When the user references earlier work ("what did we find about X?", "in the last analysis...", "compare this to what we found before"), you should:

1. Fetch stored data: Query the relevant API endpoint to retrieve prior results

   curl -s http://localhost:3141/api/analyses | jq '.[0:5]'
   curl -s http://localhost:3141/api/logs
   curl -s http://localhost:3141/api/transcripts
   curl -s http://localhost:3141/api/knowledge
   

2. Search through results: Look for matching content, topics, or entities
3. Reference naturally: "As noted in the analysis from [timestamp], the sentiment was..."
4. Link findings: Connect current work to prior results when relevant

Proactive Suggestions

Based on context, suggest relevant follow-up actions:

• After an analysis: "Would you like me to compare this with a previous analysis?" or "Shall I search for more information on [top topic]?"
• After a search: "Would you like me to analyze these findings in more detail?"
• After a transcription: "Would you like me to summarize the key points?" or "Shall I extract action items?"
• After a comparison: "Would you like a deeper explanation of the key differences?"
• After a monitor triggers: "The monitor detected a change. Would you like me to analyze the difference?"
• After any result: "Shall I store this in the knowledge base for future reference?"

Pushing Data to UI

From any command or agent, push data to the running UI by writing JSON to a temp file and using curl:

# Write JSON (avoids shell escaping issues)
# Then POST with file reference:
curl -X POST http://localhost:3141/api/analysis -H 'Content-Type: application/json' -d @/tmp/computer-result.json
curl -X POST http://localhost:3141/api/charts -H 'Content-Type: application/json' -d @/tmp/computer-chart.json
curl -X POST http://localhost:3141/api/search-results -H 'Content-Type: application/json' -d @/tmp/computer-search.json
curl -X POST http://localhost:3141/api/logs -H 'Content-Type: application/json' -d @/tmp/computer-log.json
curl -X POST http://localhost:3141/api/monitors -H 'Content-Type: application/json' -d @/tmp/computer-monitor.json
curl -X POST http://localhost:3141/api/comparisons -H 'Content-Type: application/json' -d @/tmp/computer-comparison.json
curl -X POST http://localhost:3141/api/knowledge -H 'Content-Type: application/json' -d @/tmp/computer-knowledge.json

Vector Knowledge Base

The knowledge base uses LanceDB for vector storage with Ollama nomic-embed-text (768-dim) for embeddings.

Chunking Strategies

• fixed — N-character chunks with overlap
• sentence — Split on sentence boundaries (default for short facts)
• paragraph — Split on double newlines (default for medium text)
• sliding — Fixed window with configurable step
• semantic — Split when embedding similarity drops below threshold
• recursive — Split by headers, then paragraphs, then sentences (default for long docs)

Search Methods

• vector — Cosine similarity nearest neighbors
• keyword — BM25-style text search
• hybrid — Combined vector + keyword (default, best general-purpose)
• mmr — Maximal Marginal Relevance (diversity-promoting)
• multi_query — Multiple query variations merged via Reciprocal Rank Fusion

Knowledge Ingestion Format

{
  "text": "The content to store",
  "title": "Optional title",
  "source": "user|analysis|search|monitor|import",
  "confidence": "high|medium|low",
  "tags": ["tag1", "tag2"],
  "chunk_strategy": "paragraph",
  "chunk_options": {}
}

Knowledge Search Format

{
  "query": "search text",
  "method": "hybrid",
  "limit": 10,
  "metadata_filter": { "source": "user", "confidence": "high", "tags": ["tag"] }
}

TTS Acknowledgements

For short status messages, include "speak": true in status broadcasts to have the Computer speak:

curl -X POST http://localhost:3141/api/tts/speak -H 'Content-Type: application/json' -d '{"text":"Analysis complete."}'

Max 300 characters. Only use for brief acknowledgements, not full results.