word-counter

A comprehensive text analysis tool that counts words, characters, sentences, and paragraphs. Calculates reading time, speaking time, reading level (Flesch-Kincaid), and keyword density.

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Install skill "word-counter" with this command: npx skills add openlark/word-counter

Word Counter

Overview

This skill provides comprehensive text analysis including word count, character count, sentence/paragraph count, reading/speaking time estimates, readability scoring, and keyword density analysis.

Use Cases

Use when users need to analyze text length, check word count for essays/blogs/social media posts, estimate reading time, assess readability, or analyze keyword frequency for SEO content.

Capabilities

  1. Word Count - Accurate word counting handling multiple spaces, line breaks, and special characters
  2. Character Count - With and without spaces (useful for Twitter, meta descriptions)
  3. Sentence & Paragraph Count - Track document structure
  4. Reading Time - Based on 200 words/minute average
  5. Speaking Time - Based on 130 words/minute natural pace
  6. Reading Level - Flesch-Kincaid grade level estimation
  7. Keyword Density - Top keywords ranked by frequency for SEO analysis

Common Word Count Requirements

Content TypeWord CountReading Time
X (Twitter) post40-50< 1 min
Facebook post40-80< 1 min
LinkedIn post50-100< 1 min
Email subject line6-10< 1 min
Meta description25-30< 1 min
Short blog post300-6002-3 min
Standard blog post1,000-1,5005-7 min
Long-form article2,000-3,00010-15 min
College essay500-5,0003-25 min

Usage

Basic Analysis

from scripts.word_counter import analyze_text

result = analyze_text("Your text here...")
print(result)

Command Line

python scripts/word_counter.py "Your text here"
# or
python scripts/word_counter.py --file path/to/file.txt

Scripts

  • scripts/word_counter.py - Main text analysis script

References

  • references/formulas.md - Detailed formulas for reading level and time calculations

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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