Text Analyzer Skill

A comprehensive skill for analyzing, processing, and summarizing text documents with support for multiple formats and output types.

Overview

Text Analyzer provides intelligent text processing capabilities including:

• Document summarization with multiple summary lengths
• Sentiment analysis and emotion detection
• Keyword extraction and topic modeling
• Reading level assessment
• Text statistics and metrics

Installation

curl -s https://claude.ai/skills/text-analyzer | bash

Or manually:

1. Copy text-analyzer.md to ~/.claude/skills/
2. Restart Claude Code
3. Access via /text-analyzer command

Quick Start

Basic Summarization

/text-analyzer summarize --file document.txt --length short

Sentiment Analysis

/text-analyzer sentiment --text "This is amazing!"

Extract Keywords

/text-analyzer keywords --file article.md --count 10

Configuration

Create ~/.config/text-analyzer.yaml:

summarization:
  default_length: medium
  preserve_structure: true

sentiment:
  model: bert-base
  confidence_threshold: 0.7

keywords:
  algorithm: tfidf
  min_frequency: 2

Commands

summarize

Generates summaries of text documents with configurable length.

Usage:

/text-analyzer summarize [OPTIONS]

Options:

• --file PATH - Input file path (required)
• --length {short|medium|long} - Summary length (default: medium)
• --format {text|json|markdown} - Output format (default: text)
• --preserve-structure - Keep original document structure
• --ignore-stopwords - Remove common words from summary

Example:

/text-analyzer summarize --file research.pdf --length long --format json

sentiment

Analyzes text sentiment and emotional tone.

Usage:

/text-analyzer sentiment [OPTIONS]

Options:

• --text STRING - Input text (required if no --file)
• --file PATH - Input file path (required if no --text)
• --detailed - Show detailed emotion breakdown
• --comparative - Compare sentiment across sections

Example:

/text-analyzer sentiment --file feedback.txt --detailed

keywords

Extracts key terms and topics from text.

Usage:

/text-analyzer keywords [OPTIONS]

Options:

• --file PATH - Input file path (required)
• --count N - Number of keywords to extract (default: 5)
• --min-frequency N - Minimum occurrence frequency (default: 1)
• --include-phrases - Include multi-word phrases
• --exclude-pos TAGS - Exclude parts of speech

Example:

/text-analyzer keywords --file document.md --count 15 --include-phrases

Use Cases

Document Summarization Workflow

Process large documentation automatically:

1. Store documents in a library
2. Generate summaries for quick review
3. Extract keywords for indexing
4. Assess reading difficulty

Sentiment Monitoring

Track sentiment across customer feedback:

/text-analyzer sentiment --file feedback.csv --detailed
/text-analyzer sentiment --file support_tickets.json --comparative

Content Analysis Pipeline

Build AI-powered content analysis workflows:

# Extract summary and keywords
/text-analyzer summarize --file article.md > summary.txt
/text-analyzer keywords --file article.md --count 20 > keywords.txt

# Analyze sentiment
/text-analyzer sentiment --file comments.txt --detailed > sentiment.json

Output Formats

Text Format (default)

Plain text output with clear sections.

Summary:
[summarized content]

Statistics:
- Original Length: 5,234 words
- Summary Length: 1,045 words
- Compression Ratio: 80%

JSON Format

Structured output for integration:

{
  "summary": "...",
  "statistics": {
    "original_words": 5234,
    "summary_words": 1045
  },
  "metadata": {
    "processed_at": "2026-02-03T10:30:00Z",
    "version": "1.0.0"
  }
}

Markdown Format

Enhanced markdown with formatting:

# Summary

## Key Points
- Point 1
- Point 2
- Point 3

## Metadata
**Original:** 5,234 words | **Summary:** 1,045 words

Advanced Features

Batch Processing

Process multiple files at once:

/text-analyzer batch --input-dir ./documents --output-dir ./summaries --length short

Custom Models

Use custom language models:

/text-analyzer summarize --file doc.txt --model gpt-4 --temperature 0.3

Stream Processing

Handle large files with streaming:

/text-analyzer stream --input-pipe < large_file.txt --chunk-size 10000

Performance Tips

1. For Large Files: Use --stream option to avoid memory issues
2. For Speed: Use shorter summary length settings
3. For Accuracy: Use --detailed flag with sentiment analysis
4. For Batch Jobs: Use --parallel to process multiple files

Troubleshooting

Command Not Found

Ensure skill is installed:

ls ~/.claude/skills/ | grep text-analyzer

Memory Issues with Large Files

Use streaming mode:

/text-analyzer summarize --file huge.txt --stream

Inconsistent Results

Clear cache and retry:

/text-analyzer clear-cache
/text-analyzer summarize --file document.txt

API Reference

Python Integration

from text_analyzer import TextAnalyzer

analyzer = TextAnalyzer()
summary = analyzer.summarize("path/to/file.txt", length="medium")
keywords = analyzer.extract_keywords("path/to/file.txt", count=10)
sentiment = analyzer.analyze_sentiment("This is great!")

REST API

curl -X POST http://localhost:8080/api/summarize \
  -H "Content-Type: application/json" \
  -d '{"text": "...", "length": "short"}'

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

License

MIT License - See LICENSE file for details

Support

• GitHub Issues: https://github.com/claude-team/text-analyzer/issues
• Documentation: https://claude.ai/skills/text-analyzer
• Community: https://discourse.claude.ai/c/skills/text-analyzer

Last Updated: 2026-02-03 Maintained by: Claude Team