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- Leegonzales
- Aiskills
- Example Skill
example-skill_skill
- Python
20
GitHub Stars
4
Bundled Files
3 weeks ago
Catalog Refreshed
2 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstart where the catalogue uses aiagentskills.
npx veilstart add skill leegonzales/aiskills --skill example-skill- CHANGELOG.md310 B
- LICENSE1.1 KB
- README.md1.3 KB
- SKILL.md1.8 KB
Overview
This skill analyzes text to provide word and character counts, reading time estimates, complexity metrics, and readability scores. It gives a concise report that helps you judge length, audience level, and where to focus editing. Use it to quickly quantify and interpret a document’s readability and structure.
How this skill works
The skill inspects the input text to compute basic metrics (total and unique words, characters, average word length) and estimates reading time using standard words-per-minute ranges with adjustments for complexity. It parses sentences to measure average sentence length and variance, flags long words and estimates syllable counts, then calculates Flesch Reading Ease and a US grade-level estimate. Results are returned in a compact, human-readable report with interpretation guidance.
When to use it
- You ask “how long is this text?” or need a word/character count
- Estimate reading time for articles, speeches, or transcripts
- Assess whether text is appropriate for a target audience or grade level
- Compare drafts by complexity or readability before publishing
- Prepare content to meet accessibility or editorial standards
Best practices
- Provide the full text or a representative sample for accurate metrics
- Specify target reading speed or audience if you need custom estimates
- Use the grade-level and Flesch score together to judge accessibility
- Combine with editing workflows: metrics guide where to shorten sentences or simplify vocabulary
- Run comparisons between drafts to measure improvement quantitatively
Example use cases
- Calculate reading time and word count for a blog post to set publishing expectations
- Evaluate textbook excerpts to confirm they match a target grade level
- Assess marketing copy to ensure it reads as plain language for broad audiences
- Measure complexity changes across revised drafts to track simplification efforts
- Generate baseline metrics before automated rewriting or human editing
FAQ
By default it uses 200–250 words per minute and provides a range; you can supply a different WPM for custom estimates.
How accurate are syllable and complexity estimates?
Estimates use common heuristics (character patterns and vowel clusters). They are good for comparative and approximate analysis but not perfect for every language or exceptional vocabulary.