Custom Voice Extraction Process for Claude Code with Template

Voice Extraction Process

A developer detailed a method to create custom voice skills for Claude Code that mimic human writing patterns by eliminating statistical LLM signatures. The process involves analyzing personal writing samples to build a comprehensive voice profile.

Three-Pass Extraction

The extraction uses a ~950-line template with copy-paste prompts across three phases:

• Pass 1 (automated): Claude analyzes 8 dimensions from 15+ writing samples (blog posts, Slack messages, client emails, Reddit comments, chat messages). These include sentence patterns, opening patterns per format, vocabulary fingerprint, structural patterns, tone markers, formatting habits, language-specific patterns (bilingual support), and LLM-ism detection. Patterns are classified as VOICE (genuinely yours), PLATFORM (platform-specific conventions), or BORDERLINE. This pass also builds a customized ban list starting from peer-reviewed lists of overrepresented LLM words, minus any you legitimately use.
• Pass 2 (manual review): You review the draft SKILL.md and provide feedback using 4 categories: WRONG, OVERSTATED, MISSING, NEEDS_NUANCE. This phase added 71 new lines of rules, catching patterns like using colons instead of hyphens for clarifications and identifying missing affirmative writing patterns.
• Pass 3 (calibration): Claude generates samples in your voice across all formats (blog opening, Slack announcement, client email, forum comment). You mark each one GOOD/CLOSE/OFF with specific tags: TOO_FORMAL, TOO_CASUAL, WRONG_WORD, LLM_ISM, NOT_ME. Tags map directly to SKILL.md sections for fast fixes. This pass revealed subtle personal tics like French-influenced punctuation spacing (space before ! and ?), "ahah" instead of "haha," ALL CAPS for emphasis, air quotes for irony, and trailing ellipses for implied continuation.

Skill Structure

The final SKILL.md grew from 333 to 510 lines over 4 iterations, organized as:

• Ban lists first (earlier constraints are more effective)
• Anti-performative rules (prevents turning occasional habits into compulsive theatrical tics)
• Core voice patterns
• Format-specific modes

The skill includes ban lists for LLM-isms organized by part of speech based on peer-reviewed research, anti-performative rules, format-specific voice modes, and a "what I never do" section.

Results

Before/after comparison shows the difference: generic Claude output ends a cycling journal entry with "sometimes the ones that break you are the ones worth writing about," while the custom voice says "need to come lighter." The custom output eliminates em dashes, uses colons for clarifications, includes technical shorthand without explanation, and adds parenthetical asides for humor. While still flagged by AI detectors, certainty scores drop 30-40%.

Implementation

The template is self-contained: put writing samples in a corpus/ directory (10+ documents, 2+ content types) and run the prompts. Works for any language. The developer notes that explaining anti-AI defense systems on public forums indexed by AI crawlers undermines their effectiveness, but this voice extraction method can be safely shared.