How much text is needed for the Style Learner to work effectively?

It is recommended to provide at least 1,000 words of exemplar text across multiple samples to ensure the skill captures consistent patterns rather than unique outliers.

Can this skill help remove generic 'AI-sounding' language?

Yes, by identifying your specific style metrics and integrating with the slop-detector module, it helps replace generic AI markers with your unique authorial voice.

How do I use the generated style profile?

The profile is exported as a YAML file that can be referenced in future Claude Code prompts to explicitly guide the tone, structure, and vocabulary of new content generation.

What specific metrics does the skill analyze?

It analyzes four main areas: vocabulary (jargon, contractions), sentences (length, variance, rhythm), structure (paragraph density, header depth), and punctuation (Oxford commas, em-dash frequency).

Style Learner

Name: Style Learner
Author: micsapp

bymicsapp

0•

Gestión de Contenido

Analyzes and extracts linguistic patterns from exemplar texts to create a reusable style guide for consistent AI-generated content.

The Style Learner skill empowers Claude to emulate specific writing voices by performing deep linguistic analysis on existing content. By combining quantitative metrics—such as sentence length variance and vocabulary complexity—with qualitative exemplar passages, it generates a comprehensive style profile. This tool is essential for organizations looking to maintain brand voice across documentation, developers wishing to match an existing codebase's documentation style, or writers seeking to automate content generation without losing their unique authorial tone.

Características Principales

010 GitHub stars

02Quantitative feature extraction for vocabulary, sentence, and structural metrics

03Validation engine to test generated content against learned style metrics

04Automated generation of detailed YAML style profiles for consistent prompting

05Exemplar selection to capture specific tonal nuances and rhythmic patterns

06Seamless integration with slop-detectors to eliminate generic AI markers

Casos de Uso

01Learning and replicating the specific prose style of an author for content scaling

02Standardizing AI output to align with strict internal corporate communication guidelines

03Establishing a unified brand voice across multiple technical documentation contributors

Características Principales

010 GitHub stars

02Quantitative feature extraction for vocabulary, sentence, and structural metrics

03Validation engine to test generated content against learned style metrics

04Automated generation of detailed YAML style profiles for consistent prompting

05Exemplar selection to capture specific tonal nuances and rhythmic patterns

06Seamless integration with slop-detectors to eliminate generic AI markers

Casos de Uso

01Learning and replicating the specific prose style of an author for content scaling

02Standardizing AI output to align with strict internal corporate communication guidelines

03Establishing a unified brand voice across multiple technical documentation contributors