Style Learner

# Exemplar Reference Module

Select and document representative passages for style guidance.

## Selection Criteria

Choose passages that demonstrate:

1. **Characteristic rhythm**: Sentence length variation patterns
2. **Vocabulary choices**: Typical word selection
3. **Tone markers**: How formality/informality is expressed
4. **Structural preferences**: Paragraph and list usage

## Anti-Selection Criteria

Avoid passages that:

- Are atypically long or short
- Contain unusual formatting
- Quote external sources
- Are transitional/boilerplate
- Contain code blocks (unless style includes code)

## Passage Length

Optimal exemplar length: **50-150 words**

- Too short: Insufficient pattern demonstration
- Too long: Dilutes key characteristics

## Annotation Format

```markdown
### Exemplar: Technical Explanation

**Source**: docs/architecture.md, lines 45-52
**Word count**: 87
**Demonstrates**: Concise technical explanation with grounded examples

> The cache layer sits between the API and database. When a request
> arrives, we check Redis first. Cache hits return in under 5ms;
> misses fall through to Postgres, adding 50-200ms depending on
> query complexity. We chose Redis over Memcached for its richer
> data structures—sorted sets power our leaderboard feature.

**Key characteristics**:
- Short, direct sentences (avg 12 words)
- Specific numbers (5ms, 50-200ms)
- One em dash for aside
- Trade-off explanation ("chose X over Y because")
- No filler phrases
- Technical but accessible
```

## Minimum Exemplar Set

For a complete style profile, collect at least:

| Type | Purpose |
|------|---------|
| Explanation | How concepts are introduced |
| Instruction | How steps are given |
| Transition | How sections connect |

For narrative content, add:

| Type | Purpose |
|------|---------|
| Description | Scene/object portrayal |
| Dialogue | Character voice |
| Action | Event pacing |

## Usage in Generation

When generating new content, present exemplars as reference:

```
Write in a style similar to this passage:

> [exemplar text]

Key aspects to match:
- Sentence length around X words
- [Specific vocabulary preferences]
- [Tone markers to include]
- [Patterns to avoid]
```


---
module: feature-extraction
category: style-analysis
dependencies: [Bash, Read]
estimated_tokens: 400
---

# Feature Extraction Module

Quantitative style metrics extraction from exemplar text.

## Vocabulary Analysis

```bash
# Average word length
awk '{for(i=1;i<=NF;i++){sum+=length($i);count++}}END{print sum/count}' file.md

# Unique word ratio
words=$(tr '[:space:]' '\n' < file.md | grep -v '^$' | wc -l)
unique=$(tr '[:space:]' '\n' < file.md | grep -v '^$' | sort -u | wc -l)
echo "scale=2; $unique / $words" | bc

# Contraction count
grep -oE "\b\w+'(t|s|d|ll|ve|re|m)\b" file.md | wc -l
```

## Sentence Analysis

```python
import re

def analyze_sentences(text):
    # Split on sentence boundaries
    sentences = re.split(r'[.!?]+', text)
    sentences = [s.strip() for s in sentences if s.strip()]

    lengths = [len(s.split()) for s in sentences]

    return {
        'count': len(sentences),
        'avg_length': sum(lengths) / len(lengths),
        'min_length': min(lengths),
        'max_length': max(lengths),
        'std_dev': statistics.stdev(lengths) if len(lengths) > 1 else 0,
        'questions': sum(1 for s in sentences if '?' in s),
        'fragments': sum(1 for l in lengths if l < 5)
    }
```

## Structural Analysis

```bash
# Paragraph lengths (sentences per paragraph)
awk -v RS='\n\n' '{
    gsub(/[.!?]/, "&\n");
    n = split($0, a, "\n");
    print n
}' file.md

# List ratio
bullets=$(grep -c '^\s*[-*]' file.md)
total=$(wc -l < file.md)
echo "scale=2; $bullets / $total" | bc

# Header depth
grep -E '^#{1,6}\s' file.md | head -1 | grep -o '#' | wc -c
```

## Punctuation Profile

```bash
# Per 1000 words
words=$(wc -w < file.md)

em