voice-learning

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Knowledge base for analyzing and replicating writing voice. Use when learning voice patterns or generating voice-matched content.

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When & Why to Use This Skill

This Claude skill provides a comprehensive framework for analyzing, extracting, and replicating unique writing voices to ensure brand consistency. By evaluating five key dimensions—lexical, syntactic, rhetorical, tonal, and structural—it enables the creation of highly personalized content profiles that eliminate generic AI outputs and match specific human writing patterns.

Use Cases

  • Voice Profile Creation: Analyze existing blog posts, transcripts, or social media history to build a detailed 'Voice Profile' that serves as a permanent reference for future content generation.
  • Executive Ghostwriting: Replicate a specific leader's unique communication style, including their preferred sentence structures, vocabulary, and rhetorical patterns for emails or articles.
  • Cross-Platform Brand Alignment: Adapt core messaging for different channels like LinkedIn, X (Twitter), and long-form blogs while maintaining a consistent brand personality through platform-specific adjustments.
  • Content Quality Control: Use the built-in voice matching checklist to audit AI-generated drafts, ensuring they avoid 'corporate' clichés and adhere to specific tonal constraints.
namevoice-learning
descriptionKnowledge base for analyzing and replicating writing voice. Use when learning voice patterns or generating voice-matched content.

Voice Learning Skill

This skill provides frameworks and techniques for analyzing writing voice and ensuring content generation matches a learned voice profile.

Voice Analysis Framework

The 5 Dimensions of Voice

  1. Lexical Voice - Word choice

    • Vocabulary complexity (grade level)
    • Industry jargon usage
    • Power words and phrases
    • Words consciously avoided

  2. Syntactic Voice - Sentence structure

    • Average sentence length
    • Sentence variety (simple, compound, complex)
    • Use of fragments for effect
    • Paragraph length preferences

  3. Rhetorical Voice - Persuasion patterns

    • Primary appeal (ethos, pathos, logos)
    • Use of questions
    • Analogy and metaphor frequency
    • Storytelling vs. data-driven

  4. Tonal Voice - Emotional coloring

    • Formality spectrum (1-10)
    • Warmth/distance
    • Confidence level
    • Humor integration

  5. Structural Voice - Organization

    • Opening patterns (hook types)
    • Transition preferences
    • Closing/CTA style
    • Use of formatting elements

Voice Extraction Techniques

From Transcripts

Transcripts reveal natural, unfiltered voice:

  • Listen for filler phrases - These often carry personality
  • Note explanation patterns - How do they break down complex ideas?
  • Capture spontaneous analogies - Unplanned comparisons reveal thinking
  • Track energy shifts - What topics generate enthusiasm?

From Written Content

Published writing shows intentional voice:

  • Analyze hooks - First sentences reveal attention strategy
  • Study transitions - How do ideas connect?
  • Examine conclusions - What's the signature close?
  • Note formatting - Headers, bullets, bold usage

From Social Media

Social content shows engagement voice:

  • Opening hooks - How is scrolling stopped?
  • Thread structure - How are ideas chunked?
  • Engagement prompts - Questions, CTAs
  • Community signals - In-group references

Voice Matching Checklist

Before generating content, verify:

  • Vocabulary matches profile (no words from "avoid" list)
  • Sentence rhythm matches (length, variety)
  • Opening style matches learned pattern
  • Tone is consistent (formality, warmth)
  • Frameworks/analogies are in-character
  • CTA style matches preference

Memory Storage Schema

Store voice patterns with these categories:

Categories: ["voice-profile", "<dimension>"]

Dimensions:
- "lexical" - Word choice patterns
- "syntactic" - Sentence structure
- "rhetorical" - Persuasion patterns
- "tonal" - Emotional coloring
- "structural" - Organization patterns
- "topic-specific" - Voice variations by subject
- "platform-specific" - Voice variations by channel

Voice Injection for Content Generation

When generating content in user's voice:

  1. Load Profile

    Recall voice-profile patterns from memory

  2. Apply Constraints

    • Use vocabulary from profile
    • Match sentence structure patterns
    • Apply tonal settings
    • Follow structural preferences

  3. Verify Match

    • Read output aloud mentally
    • Check against "avoid" list
    • Confirm opening/closing match patterns

  4. Note Deviations

    • If topic requires different voice, note it
    • Flag for user review if uncertain

Platform-Specific Voice Adjustments

LinkedIn

  • Slightly more formal
  • Professional but personable
  • Thought leadership framing
  • Engagement-focused endings

Twitter/X

  • More casual, punchy
  • Thread-optimized structure
  • Hook-heavy openings
  • Community engagement signals

Blog/Long-form

  • Full voice expression
  • Story-driven when appropriate
  • Technical depth as needed
  • Signature frameworks

Email

  • Direct and action-oriented
  • Relationship-appropriate formality
  • Clear next steps

Common Voice Pitfalls

  1. Over-formalizing - AI tendency to sound corporate
  2. Losing quirks - Removing personality for "polish"
  3. Inconsistent tone - Shifting formality mid-piece
  4. Wrong jargon - Using terms outside their vocabulary
  5. Generic CTAs - Not matching their engagement style

Voice Evolution

Voice changes over time. Periodically:

  • Analyze recent content (last 90 days)
  • Compare to older profile entries
  • Update patterns that have shifted
  • Archive outdated patterns (don't delete)