---
name: computer-operations
description: |
  Use this skill when the user mentions "Computer", "LCARS", "Enterprise computer", "start the computer", "computer analyze", "computer search", "computer transcribe", "captain's log", "monitor", "briefing", "compare", "summarize", "explain", "translate", "knowledge", "remember", "pipeline", "export", "report", or references the Star Trek computer interface. Provides operational knowledge for the Computer plugin system including server management, API endpoints, agent integration, data storage, cross-referencing prior results, desktop notifications, smart voice routing, and proactive suggestions.
version: 3.0.0
tools: Read, Bash
---

# 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
   ```bash
   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:
```bash
# 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
```json
{
  "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
```json
{
  "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:
```bash
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.