Slash Command

/analyze

Run AI analysis on text, files, or context

Install

Run in your terminal

npx claudepluginhub chendren/computer

Details

Argument<text-or-file-path>

Allowed Tools

ReadBashWriteTaskWebSearch

Command Content

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Stats

Stars1

Forks1

Last CommitFeb 14, 2026

Actions

View Source View Plugin View on GitHub View README

Process

Determine Input: Examine $ARGUMENTS

If it looks like a file path (starts with / or ~, or has a file extension), read the file
If it's text, use directly
If empty, inform the user they need to provide text or a file path

Perform Analysis: Analyze the content and produce:

Sentiment: Overall tone (positive/negative/neutral/mixed) with confidence and breakdown percentages
Topics: 3-7 key themes with relevance scores (0-1)
Action Items: Actionable items with priority (high/medium/low)
Summary: Concise 2-3 sentence summary
Entities: People, organizations, locations, dates, technical terms

Generate Chart Spec: Create a Chart.js config for visualizing topic distribution using LCARS colors:

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

Push to UI: Write the full analysis JSON to /tmp/computer-analysis-result.json using the Write tool, then POST it:

curl -s -X POST http://localhost:3141/api/analysis -H 'Content-Type: application/json' -d @/tmp/computer-analysis-result.json

The JSON MUST include an "input" field containing the original raw text that was analyzed, plus all analysis fields and a chartSpec with a valid Chart.js v4 config. Example structure:

{
  "input": "the original raw text that was analyzed...",
  "summary": "...",
  "sentiment": {...},
  "topics": [...],
  "actionItems": [...],
  "entities": {...},
  "chartSpec": {...}
}

Display: Show analysis results in the terminal as well, formatted clearly.

Computer Analyze

Run comprehensive AI analysis on the provided input.

Process

Determine Input: Examine $ARGUMENTS
- If it looks like a file path (starts with / or ~, or has a file extension), read the file
- If it's text, use directly
- If empty, inform the user they need to provide text or a file path
Perform Analysis: Analyze the content and produce:
- Sentiment: Overall tone (positive/negative/neutral/mixed) with confidence and breakdown percentages
- Topics: 3-7 key themes with relevance scores (0-1)
- Action Items: Actionable items with priority (high/medium/low)
- Summary: Concise 2-3 sentence summary
- Entities: People, organizations, locations, dates, technical terms
Generate Chart Spec: Create a Chart.js config for visualizing topic distribution using LCARS colors:
- Colors: #FF9900, #CC99CC, #9999FF, #FF9966, #CC6699, #99CCFF, #FFCC00
- Background: #000000, Text: #FF9900, Grid: #333333

Push to UI: Write the full analysis JSON to /tmp/computer-analysis-result.json using the Write tool, then POST it:

curl -s -X POST http://localhost:3141/api/analysis -H 'Content-Type: application/json' -d @/tmp/computer-analysis-result.json

The JSON MUST include an "input" field containing the original raw text that was analyzed, plus all analysis fields and a chartSpec with a valid Chart.js v4 config. Example structure:

{
  "input": "the original raw text that was analyzed...",
  "summary": "...",
  "sentiment": {...},
  "topics": [...],
  "actionItems": [...],
  "entities": {...},
  "chartSpec": {...}
}

Display: Show analysis results in the terminal as well, formatted clearly.