smithery.ai

style-learner

Extracts writing style patterns from exemplar text into a reusable profile. Use when creating a style guide or learning a specific author's voice.

First seen Mar 27, 2026

Installation

$ npx skills add https://smithery.ai

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 6,938 B
  • docs SUMMARY.md 701 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 3 installs

SKILL.md

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.

When NOT To Use

  • Extracting a person's voice from their samples (use

scribe:voice-extract)

  • Reviewing text against a profile (use scribe:voice-review)

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.

  1. 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