style-learner

name	style-learner
description	Extracts writing style patterns from exemplar text into a reusable profile. Use when creating a style guide or learning a specific author's voice.
globs	**/*.md
alwaysApply	false
category	writing-quality
tags	["style","voice","tone","exemplar","learning","consistency"]
tools	[]
complexity	medium
model_hint	standard
estimated_tokens	1800
progressive_loading	true
modules	["modules/feature-extraction.md","modules/exemplar-reference.md","modules/style-application.md"]
dependencies	["scribe:shared","scribe:slop-detector"]

Style Learning Skill

A style profile is metrics plus exemplars. Either alone is too weak to reproduce a voice.

Extract style from exemplar text and codify it as a profile that downstream skills (scribe:doc-generator, scribe:voice-generate) can apply consistently.

Approach: Feature Extraction and Exemplar Reference

The skill combines two methods because each fails alone:

1. Feature Extraction: quantifiable metrics (sentence length distribution, vocabulary complexity, structural patterns). Reproducible but soulless.
2. Exemplar Reference: specific passages that demonstrate the target style. Vivid but hard to apply at scale.

Together they form a profile precise enough to score new text and rich enough to guide rewrites. Metrics catch what exemplars miss. Exemplars carry what metrics flatten.

Required TodoWrite Items

1. style-learner:exemplar-collected - Source texts gathered
2. style-learner:features-extracted - Quantitative metrics computed
3. style-learner:exemplars-selected - Representative passages identified
4. style-learner:profile-generated - Style guide created
5. style-learner:validation-complete - Profile tested against new content

Step 1: Collect Exemplar Text

Gather representative samples of the target style.

Minimum requirements:

• At least 1000 words of exemplar text
• Multiple samples preferred (shows consistency)
• Same genre/context as target output

## Exemplar Sources

| Source | Word Count | Type |
|--------|------------|------|
| README.md | 850 | Technical |
| blog-post-1.md | 1200 | Narrative |
| api-guide.md | 2100 | Reference |

Step 2: Feature Extraction

Load: @modules/feature-extraction.md

Vocabulary Metrics

Metric	How to Measure	What It Indicates
Average word length	chars/word	Complexity level
Unique word ratio	unique/total	Vocabulary breadth
Jargon density	technical terms/100 words	Audience level
Contraction rate	contractions/sentences	Formality

Sentence Metrics

Metric	How to Measure	What It Indicates
Average length	words/sentence	Complexity
Length variance	std dev of lengths	Natural variation
Question frequency	questions/100 sentences	Engagement style
Fragment usage	fragments/100 sentences	Stylistic punch

Structural Metrics

Metric	How to Measure	What It Indicates
Paragraph length	sentences/paragraph	Density
List ratio	bullet lines/total lines	Format preference
Header depth	max header level	Organization style
Code block frequency	code blocks/1000 words	Technical density

Punctuation Profile

Metric	Normal Range	Style Indicator
Em dash rate	0-3/1000 words	Parenthetical style
Semicolon rate	0-2/1000 words	Formal complexity
Exclamation rate	0-1/1000 words	Enthusiasm level
Ellipsis rate	0-1/1000 words	Trailing thought style

Step 3: Exemplar Selection

Load: @modules/exemplar-reference.md

Select 3-5 passages (50-150 words each) that best represent the target style.

Selection criteria:

• Demonstrates characteristic sentence rhythm
• Shows typical vocabulary choices
• Represents the desired tone
• Avoids atypical or exceptional passages

Exemplar Template

### Exemplar 1: [Label]
**Source**: [filename, lines X-Y]
**Demonstrates**: [what aspect of style]

> [Quoted passage]

**Key characteristics**:
- [Observation 1]
- [Observation 2]

Step 4: Generate Style Profile

Combine extracted features and exemplars into a usable style guide.

Profile Format

# Style Profile: [Name]
# Generated: [Date]
# Exemplar sources: [List]

voice:
  tone: [professional/casual/academic/conversational]
  perspective: [first-person/third-person/second-person]
  formality: [formal/neutral/informal]

vocabulary:
  average_word_length: X.X
  jargon_level: [none/light/moderate/heavy]
  contractions: [avoid/occasional/frequent]
  preferred_terms:
    - "use" over "utilize"
    - "help" over "facilitate"
  avoided_terms:
    - delve
    - leverage
    - comprehensive

sentences:
  average_length: XX words
  length_variance: [low/medium/high]
  fragments_allowed: [yes/no/sparingly]
  questions_used: [yes/no/sparingly]

structure:
  paragraphs: [short/medium/long] (X-Y sentences)
  lists: [prefer prose/balanced/prefer lists]
  headers: [descriptive/terse/question-style]

punctuation:
  em_dashes: [avoid/sparingly/freely]
  semicolons: [avoid/sparingly/freely]
  oxford_comma: [yes/no]

exemplars:
  - label: "[Exemplar 1 label]"
    text: |
      [Quoted passage]
  - label: "[Exemplar 2 label]"
    text: |
      [Quoted passage]

anti_patterns:
  - [Pattern to avoid 1]
  - [Pattern to avoid 2]

Step 5: Validation

Test the profile against new content:

1. Generate sample content using the profile
2. Compare metrics to extracted features
3. Have user evaluate voice/tone match
4. Refine profile based on feedback

Validation Checklist

• Metrics within 20% of exemplar averages
• No anti-pattern violations
• Tone matches user expectation
• Vocabulary aligns with exemplars
• Structure follows profile guidelines

Usage in Generation

When generating new content, reference the profile:

Generate [content type] following the style profile:
- Voice: [from profile]
- Sentence length: target ~[X] words, vary between [Y-Z]
- Use exemplar passage as tone reference:
  > [exemplar quote]
- Avoid: [anti-patterns from profile]

Module Reference

• See modules/style-application.md for applying learned styles to new content

Integration with slop-detector

After generating content, run slop-detector to verify:

1. No AI markers introduced
2. Style metrics match profile
3. Anti-patterns avoided

Exit Criteria

• Style profile document created
• At least 3 exemplar passages included
• Quantitative metrics extracted
• Anti-patterns from slop-detector integrated
• Validation test passed