style-learner
Learn and extract writing style patterns from exemplar text for consistent
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/athola/nm-scribe-style-learnerNight Market Skill — ported from claude-night-market/scribe. For the full experience with agents, hooks, and commands, install the Claude Code plugin.
Style Learning Skill
Extract and codify writing style from exemplar text for consistent application.
Approach: Feature Extraction + Exemplar Reference
This skill combines two complementary methods:
- Feature Extraction: Quantifiable style metrics (sentence length, vocabulary complexity, structural patterns)
- Exemplar Reference: Specific passages that demonstrate desired style
Together, these create a comprehensive style profile that can guide content generation and editing.
Required TodoWrite Items
style-learner:exemplar-collected- Source texts gatheredstyle-learner:features-extracted- Quantitative metrics computedstyle-learner:exemplars-selected- Representative passages identifiedstyle-learner:profile-generated- Style guide createdstyle-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
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-athola-nm-scribe-style-learner": {
"enabled": true,
"auto_update": true
}
}
}Related Skills
extract
Analyze a codebase and build a knowledge base of business logic, architecture, data flow, and engineering patterns. The foundation for gauntlet challenges and agent integration
discourse
>- Scan community discussion channels (HN, Lobsters, Reddit, tech blogs) for experience reports and opinions on a topic
synthesize
>- Merge, deduplicate, rank, and format research findings from multiple channels into a coherent report. Use after research agents return their results
workflow-monitor
Detect workflow failures and inefficient patterns, then create GitHub issues for improvement via /fix-workflow
architecture-paradigm-hexagonal
Hexagonal (Ports and Adapters) architecture isolating domain logic from infrastructure