An efficient text analysis tool for calculating readability scores, word counts, and keyword density with zero dependencies.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install text-stats
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install text-stats using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Text Stats?
Text Stats is a lightweight, zero-dependency Python utility designed for deep text document analysis. It empowers developers and content creators within the Openclaw Skills ecosystem to evaluate writing quality through metrics like Flesch-Kincaid Grade level and Gunning Fog Index. By providing detailed insights into sentence structure and paragraph length, it helps refine communication for specific target audiences.
Beyond basic metrics, this skill offers robust support for multi-language environments, including specialized CJK character counting where Chinese characters are treated as individual words. Whether you are performing SEO audits or improving technical documentation, Text Stats provides the raw data needed to optimize engagement and clarity.
Text Stats Use Cases
- Optimizing blog posts and articles for target readability levels to improve SEO rankings.
- Conducting keyword density audits to prevent over-optimization in digital marketing content.
- Measuring estimated reading time for newsletters and documentation to improve user experience.
- Analyzing academic or technical papers for complexity using Flesch Reading Ease scores.
- Automating word count tracking for multi-language (English and CJK) writing projects.
How Text Stats Works
- The user provides a text or markdown file path to the text-stats command-line interface.
- The script parses the document content using the Python standard library, ensuring high performance without external packages.
- It executes specific algorithms to calculate word, sentence, and paragraph counts, adjusting for CJK character sets if detected.
- Advanced readability formulas like Flesch-Kincaid and Gunning Fog are applied to the processed text data.
- The skill generates a structured report or specific data point (like word count or top keywords) based on the user's flags.
Text Stats Setup
To start using Text Stats within your Openclaw Skills environment, ensure you have Python 3 installed. No additional pip installations are required.
# Verify your Python installation
python3 --version
# Run a full analysis on your document
python3 scripts/text_stats.py analyze path/to/your/document.md
Text Stats Data Schema & Taxonomy
The skill processes text data and outputs statistics in a human-readable format. The metrics captured include:
| Metric | Description |
|---|---|
| Basic Counts | Words, Characters, Sentences, Paragraphs |
| Readability | Flesch-Kincaid Grade, Reading Ease, Gunning Fog |
| SEO Data | Keyword frequency, density percentage, top-N keywords |
| Time | Estimated reading time based on a 250 wpm average |
Text Stats Advanced Features
- Keyword Density Filtering: Use the --top flag to extract a specific number of dominant keywords for SEO analysis.
- Multilingual Support: Native handling of CJK characters ensures accurate metrics for global Openclaw Skills projects.
- Specific Extractors: Run focused commands like wc or time to get immediate single-point metrics without full analysis.
- Zero Dependency Architecture: Built entirely on the Python standard library for maximum portability and security.
SKILL.md
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