textstat Python Readability Statistics Calculator
textstat is a Python package that calculates readability statistics for text content, including Flesch Reading Ease, Gunning Fog Index, SMOG Index, Coleman-Liau Index, and Dale-Chall score. It provides quantitative readability metrics that content writers and SEO professionals use to optimize content for target audiences.
npx skills add agentskillexchange/skills --skill textstat-python-readability-statistics-calculator
textstat is an open-source Python library for calculating readability statistics on text objects including paragraphs, sentences, and full articles. It implements over a dozen established readability formulas used by educators, content strategists, and SEO professionals to assess how accessible written content is for different audiences.
What this skill actually does
Supported Readability Metrics
textstat provides implementations of the most widely used readability scoring systems:
- Flesch Reading Ease: Scores text on a 0-100 scale where higher scores indicate easier reading. The most commonly used readability metric.
- Flesch-Kincaid Grade Level: Translates readability into a U.S. school grade level, making it intuitive for content targeting.
- Gunning Fog Index: Estimates years of formal education needed to understand the text on first reading.
- SMOG Index: Calculates the grade level required to comprehend a piece of writing based on polysyllable count.
- Coleman-Liau Index: Uses character counts rather than syllable counts for grade-level estimation.
- Dale-Chall Readability Score: Compares words against a list of 3,000 familiar words to assess difficulty.
- Automated Readability Index (ARI): Uses character and word counts for machine-friendly grade-level scoring.
- Linsear Write Formula: Measures readability based on sentence length and polysyllable density.
Additional Text Statistics
Beyond readability scores, textstat calculates fundamental text metrics: syllable count, lexicon count (word count), sentence count, character count, average sentence length, average syllables per word, and average letters per word. These atomic metrics are useful for building custom readability heuristics.
Integration for Content Optimization
textstat installs via pip and requires no external dependencies. It works with plain text strings, making it easy to integrate into content pipelines. SEO and content teams can use it to enforce readability targets before publishing, compare content against competitor benchmarks, or batch-analyze existing content libraries to identify pages that need simplification. The consensus_based_readability function aggregates multiple formulas into a single grade-level estimate for quick assessment.
Agent Skill Applications
An agent skill built on textstat can automatically score draft content against readability targets, flag overly complex passages, and track readability trends across a content portfolio. Combined with other content analysis tools, it enables automated editorial quality gates in publishing workflows.