Style Analyzer Skill | Agent Skills

Style Analyzer

Overview

Extract a structured, quantitative writing style profile from text samples. The profile captures vocabulary patterns, sentence structure, tone markers, and formatting preferences as measurable metrics that can constrain downstream content generation.

Core principle: Style is measurable. Every writer produces a statistical fingerprint across vocabulary, syntax, and formatting dimensions.

When to Use

Before generating content that should match a specific author's voice
When building a reusable style profile for brand voice consistency
When comparing writing styles across multiple authors or documents
When auditing content for style drift from an established voice

Workflow

Step 1: Ingest Text Samples

Accept text from one or more sources:

# Analyze a single file
node .claude/tools/cli/style-profiler.cjs /path/to/sample.txt

# Analyze a directory of text files
node .claude/tools/cli/style-profiler.cjs /path/to/samples/

# Output is written to .claude/context/data/user-style-profile.json

Alternatively, read files with the Read tool and analyze inline for smaller samples.

Minimum input requirements:

At least 3 text samples for reliable metrics
Each sample should be 200+ words
Samples should represent the author's typical output (not edge cases)

Step 2: Extract Vocabulary Patterns

Analyze word usage across all samples:

Tokenize all text into words (lowercase, strip punctuation)
Remove stop words (the, a, is, are, etc.)
Compute frequency distribution of remaining words
Extract top 50 vocabulary (most frequently used content words)
Calculate type-token ratio (unique words / total words) as vocabulary richness score
Identify signature phrases (2-3 word combinations that appear 3+ times)

Step 3: Analyze Sentence Structure

Measure syntactic patterns:

Average sentence length (words per sentence)
Sentence length variance (standard deviation)
Short sentence ratio (sentences under 8 words / total sentences)
Long sentence ratio (sentences over 25 words / total sentences)
Question frequency (questions / total sentences)
Clause complexity (average commas per sentence as a proxy)

Step 4: Identify Tone Markers

Score the writing on five dimensions (each 1.0 to 5.0):

| Dimension | 1.0 (Low) | 5.0 (High) | How Measured | | ---------- | ----------- | ------------- | ----------------------------------------------- | | Formality | Very formal | Very casual | Contraction frequency, slang usage, punctuation | | Directness | Hedged | Blunt | Hedge word frequency (maybe, perhaps, somewhat) | | Emotion | Neutral | Expressive | Exclamation marks, emotional adjectives | | Humor | Serious | Playful | Parenthetical asides, informal interjections | | Authority | Tentative | Authoritative | Imperative sentences, certainty language |

Step 5: Detect Formatting Preferences

Analyze structural patterns:

Average paragraph length (sentences per paragraph)
Heading depth (H1 only, H1-H2, H1-H3, etc.)
List frequency (bulleted/numbered lists per 1000 words)
Code block frequency (code blocks per 1000 words, if technical)
Bold/italic usage (emphasis markers per 1000 words)
Punctuation signature (em-dash frequency, semicolon frequency, ellipsis frequency)

Step 6: Build Style Profile JSON

Assemble all metrics into a structured profile:

{
  "version": "1.0.0",
  "createdAt": "2026-03-21T00:00:00Z",
  "sampleCount": 5,
  "totalWords": 12450,
  "vocabulary": {
    "topWords": ["specific", "pattern", "implementation", "..."],
    "typeTokenRatio": 0.42,
    "signaturePhrases": ["in practice", "the key insight"]
  },
  "sentenceStructure": {
    "avgLength": 18.3,
    "lengthVariance": 7.2,
    "shortSentenceRatio": 0.15,
    "longSentenceRatio": 0.22,
    "questionFrequency": 0.08,
    "avgCommasPerSentence": 1.4
  },
  "tone": {
    "formality": 2.8,
    "directness": 4.1,
    "emotion": 2.0,
    "humor": 1.5,
    "authority": 3.8
  },
  "formatting": {
    "avgParagraphLength": 3.2,
    "headingDepth": 3,
    "listFrequencyPer1000": 2.1,
    "codeBlockFrequencyPer1000": 0.8,
    "emphasisFrequencyPer1000": 4.5,
    "punctuation": {
      "emDashFrequency": 0.03,
      "semicolonFrequency": 0.01,
      "ellipsisFrequency": 0.005,
      "exclamationFrequency": 0.02
    }
  }
}

Save to .claude/context/data/user-style-profile.json.

Iron Laws

ALWAYS require minimum 3 text samples before computing a style profile -- fewer samples produce unreliable metrics that do not generalize.
NEVER include stop words in the top-50 vocabulary list -- stop words are universal and carry no style signal.
ALWAYS save the profile to .claude/context/data/user-style-profile.json -- downstream skills depend on this exact path.
NEVER treat a single metric in isolation as the style fingerprint -- style is the combination of all dimensions; individual metrics can coincide across very different authors.
ALWAYS include the sampleCount and totalWords fields in the profile -- consumers need to assess the statistical reliability of the metrics.

Anti-Patterns

| Anti-Pattern | Why It Fails | Correct Approach | | ------------------------------------------ | ------------------------------------------------------ | -------------------------------------------------------------- | | Analyzing a single short sample | Insufficient data; metrics reflect one mood, not style | Require 3+ samples of 200+ words each | | Including stop words in vocabulary metrics | Universal words add noise; no discriminative power | Filter all stop words before frequency analysis | | Outputting profile to a non-standard path | Downstream skills cannot find the profile | Always write to .claude/context/data/user-style-profile.json | | Treating tone as a single number | Tone is multidimensional; a single score loses nuance | Score all five dimensions independently | | Skipping formatting analysis | Style includes structure, not just words | Always analyze paragraph length, heading depth, list usage |

Assigned Agents

This skill is used by:

voice-replicator-agent -- Primary consumer for style-constrained content generation

Memory Protocol (MANDATORY)

Before starting:

node .claude/lib/memory/memory-search.cjs "style analysis text profiling"

Read .claude/context/memory/learnings.md

After completing:

New style pattern discovered -> .claude/context/memory/learnings.md
Issue with analysis -> .claude/context/memory/issues.md
Decision about metrics -> .claude/context/memory/decisions.md

ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.

Agent Skills: Style Analyzer

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