voice-injection-rewriter

Voice Injection Rewriter

Transform AI-generated text into authentic, voice-consistent content — not by faking humanity, but by applying real voice patterns from a specific person or brand.

When to Use This Skill

Use this skill when you need to:

• Rewrite AI drafts to match a specific person's voice
• Strip AI fingerprints from generated content before publishing
• Enforce voice consistency across AI-assisted content production
• Bridge the gap between AI efficiency and authentic brand expression
• Post-process any AI output (blog posts, emails, social, landing pages)

This skill is NOT a generic "humanizer." It requires voice input — either from brand-voice-learner output, ClawFu brand memory, or voice samples you provide.

Why this matters: Generic humanizers add fake imperfections (random typos, forced contractions) to trick detectors. That's an arms race you lose. This skill applies YOUR voice patterns — which produces naturally human text because it IS the voice of a real human.

Methodology Foundation

Core Principle: AI text sounds artificial not because it lacks typos, but because it lacks a specific person's vocabulary, rhythm, opinions, and structural habits. The fix is voice injection, not cosmetic imperfection.

Sources:

• NN/g Voice and Tone research
• Brand voice analysis methodology (ClawFu brand-voice-learner)
• AI detection pattern research (GPTZero, Originality.ai signal analysis)
• WhatsApp IA NDD Camp community insights on AI content workflows

The 201 insight: Most people try to make AI text "sound human" (generic). The actual skill is making AI text "sound like ME" (specific). The first is commodity. The second is craft.

What Claude Does vs What You Decide

| Claude Does | You Decide | |-------------|------------| | Applies voice patterns to rewrite text | Which voice profile to use | | Strips AI detection signals | How far to deviate from the original | | Identifies voice mismatches | Final approval of tone and accuracy | | Suggests voice-consistent alternatives | Where factual precision overrides voice | | Runs enforcement checklists | Publication context and audience |

Instructions

Prerequisites

Before using this skill, you need ONE of these:

| Voice Source | How to Get It | Quality | |-------------|---------------|---------| | ClawFu brand memory | get_brand [name] — pre-stored voice profile | Best | | brand-voice-learner output | Run the analysis skill on 10+ writing samples | Very good | | 3-5 writing samples | Paste examples of the target voice | Good | | Voice description | "Analytical, dry, French, no buzzwords, short sentences" | Minimum viable |

Phase 1: Voice Profile Load

Start by establishing the voice you're targeting.

If using ClawFu brand memory:

Load brand voice for [name]. Apply it to rewrite the following text.

If providing samples:

Here are 3 examples of my writing voice:
[Sample 1]
[Sample 2]
[Sample 3]

Extract the voice patterns, then rewrite this AI-generated text to match:
[AI text]

If describing voice:

My voice profile:
- Register: [analytical/casual/authoritative/conversational]
- Sentence length: [short punchy / mixed / long flowing]
- Contractions: [always / sometimes / never]
- Opinions: [strong and stated / subtle / neutral]
- Vocabulary: [technical / accessible / mixed]
- Signature moves: [specific habits — e.g., "Ce n'est pas X. C'est Y."]
- Forbidden words: [list any words you never use]

Rewrite this to match:
[AI text]

Phase 2: AI Fingerprint Detection

Before rewriting, identify what makes the text sound AI-generated.

AI Detection Signals (What to Flag)

Structural signals:

• Uniform sentence length (AI defaults to 15-20 words per sentence)
• Predictable paragraph structure (topic sentence → 3 supporting points → transition)
• Symmetrical lists (every bullet same length, same grammatical structure)
• Perfect parallelism in headers

Vocabulary signals:

• Corporate buzzwords nobody actually says: "delve," "landscape," "leverage," "robust," "streamline," "facilitate," "comprehensive"
• Filler openings: "In today's rapidly evolving...", "It's important to note that...", "In order to..."
• Over-hedging: "It's worth noting," "One might argue," "It could be said"
• Transition addiction: "Moreover," "Furthermore," "Additionally," "However" (every paragraph)

Tonal signals:

• No opinions (AI stays neutral by default)
• No contractions (AI under-uses them)
• Equal weight to everything (AI doesn't emphasize or deprioritize)
• Sanitized language (no edge, no personality, no risk)

Flag template:

AI FINGERPRINT SCAN:
□ Uniform sentence length? [yes/no]
□ Buzzword count: [N] instances
□ Transition density: [N] per paragraph
□ Contractions present? [yes/no]
□ Opinions/personality present? [yes/no]
□ Any sentence you'd never actually say? [list]

Phase 3: Voice Injection Rewrite

Apply the voice profile to the flagged text. This is the core operation.

The 5-Pass Rewrite

Pass 1 — Kill AI vocabulary

Replace every AI-default word with the voice-appropriate equivalent:

| AI Default | Generic Human | Voice-Specific (example) | |------------|--------------|-------------------------| | "Delve into" | "dig into" | Depends on voice — could be "creuser," "regarder de plus près," "explorer" | | "Leverage" | "use" | Voice may prefer "exploiter," "utiliser," or "s'appuyer sur" | | "Landscape" | "space" | Voice may prefer "marché," "écosystème," or just name the thing | | "Robust" | "strong" | Voice may prefer "solide," "fiable," or a domain-specific term | | "Moreover" | "Plus" | Voice may prefer "Et," "D'ailleurs," or nothing at all | | "Ensure" | "make sure" | Voice may prefer "vérifier," "s'assurer," or just drop it | | "Comprehensive" | "full" | Voice may prefer "complet," "exhaustif," or cut the word entirely | | "It is important to note" | (cut) | Replace with direct statement of the thing |

Pass 2 — Break rhythm uniformity

• Find the longest streak of same-length sentences. Break it.
• Add 2-3 short punches per section (under 8 words). "That's the point." "Not even close."
• Allow 1-2 longer flowing sentences if the voice uses them
• Fragments are OK if the voice uses fragments
• Dashes—for emphasis—if the voice does that
• Parentheticals (if the voice thinks while writing)

Pass 3 — Inject voice-specific patterns

Pull from the voice profile:

• Signature phrases or rhetorical moves
• Preferred sentence openers
• Opinion-stating patterns ("Ce n'est pas X. C'est Y." / "Here's the thing" / "Look,")
• Domain-specific vocabulary the voice always uses
• Structural habits (bullets vs. flowing prose, numbered lists vs. paragraphs)

Pass 4 — Contraction and register pass

• Apply contraction level matching the voice (some voices never contract, some always do)
• Check register: is the formality level consistent with the voice?
• Verify pronoun usage matches (some voices are "nous," some are "je," some are "on")

Pass 5 — Read-aloud test

Read the full text aloud. For each sentence ask:

• Would [person] actually say this?
• Does it flow at their pace?
• Is there any word they'd never use?

If any sentence fails, rewrite that sentence from scratch in the voice.

Phase 4: Output Format

Standard Output

## Original (AI-generated)
> [The input text]

## Rewritten (Voice: [Name/Brand])
[The rewritten text]

## Changes Made
- **Vocabulary**: [3-5 key word swaps]
- **Rhythm**: [What changed structurally]
- **Voice markers**: [What was injected from the voice profile]
- **Removed**: [What AI signals were stripped]

## Voice Consistency Score
[High/Medium/Low] — [one-line explanation]

Quick Mode (For batch processing)

If rewriting multiple pieces, skip the detailed output:

[Rewritten text only]

---
*Voice: [Name] | Swaps: [N] | Score: [H/M/L]*