Voice Clone Generator Skill

Voice Clone Generator

Overview

Generate new content that authentically mimics a specific author's writing style. Uses the structured style profile produced by style-analyzer to construct generation constraints, then verifies output quality by comparing against the original samples.

Core principle: Generation without constraints produces generic text. The style profile is the contract between analysis and generation.

When to Use

After style-analyzer has produced a style profile at .claude/context/data/user-style-profile.json
When generating blog posts, emails, documentation, or any prose in someone's voice
When maintaining brand voice consistency across multiple content pieces
When ghostwriting content that must read as if written by a specific person

Prerequisites

A valid style profile must exist at .claude/context/data/user-style-profile.json
At least one original sample must be available for A/B comparison
The content topic or brief must be provided by the user

Workflow

Step 1: Load Style Profile

Read the style profile and validate it has the required fields:

const profile = JSON.parse(
  fs.readFileSync('.claude/context/data/user-style-profile.json', 'utf-8')
);

// Validate required sections exist
const required = ['vocabulary', 'sentenceStructure', 'tone', 'formatting'];
for (const section of required) {
  if (!profile[section]) {
    throw new Error(`Style profile missing required section: ${section}`);
  }
}

If the profile does not exist, invoke Skill({ skill: 'style-analyzer' }) first.

Step 2: Construct Style-Constrained System Prompt

Build a system prompt that encodes the style profile as generation constraints:

You are writing in the voice of a specific author. Follow these constraints precisely:

VOCABULARY:
- Prefer these words when applicable: [top 20 from profile.vocabulary.topWords]
- Use these signature phrases naturally: [profile.vocabulary.signaturePhrases]
- Vocabulary richness target: [profile.vocabulary.typeTokenRatio] type-token ratio

SENTENCE STRUCTURE:
- Target average sentence length: [profile.sentenceStructure.avgLength] words
- Mix short sentences ([shortSentenceRatio]%) with longer ones ([longSentenceRatio]%)
- Use questions at [questionFrequency]% frequency
- Average [avgCommasPerSentence] commas per sentence for clause complexity

TONE:
- Formality level: [profile.tone.formality]/5.0 ([interpret: 1=very formal, 5=very casual])
- Directness: [profile.tone.directness]/5.0 ([interpret: 1=hedged, 5=blunt])
- Emotional expression: [profile.tone.emotion]/5.0
- Humor: [profile.tone.humor]/5.0
- Authority: [profile.tone.authority]/5.0

FORMATTING:
- Paragraphs should average [profile.formatting.avgParagraphLength] sentences
- Use heading depth up to H[profile.formatting.headingDepth]
- Include approximately [profile.formatting.listFrequencyPer1000] lists per 1000 words
- [If emDashFrequency > 0.02: "Use em-dashes frequently"]
- [If exclamationFrequency < 0.01: "Avoid exclamation marks"]

Step 3: Generate Content

Using the constructed system prompt, generate the requested content. The generation should:

Follow the topic/brief provided by the user
Adhere to all style constraints from Step 2
Be original text -- not copied from the samples
Match the approximate length requested by the user

Step 4: A/B Compare with Original Samples

After generation, compare the output against the original samples on these dimensions:

| Metric | How to Measure | Acceptable Deviation | | -------------------- | ----------------------------------------------- | --------------------- | | Avg sentence length | Count words per sentence in generated text | Within 20% of profile | | Vocabulary overlap | % of top-50 words that appear in generated text | At least 40% | | Tone formality | Re-score generated text on formality scale | Within 0.5 of profile | | Paragraph length | Count sentences per paragraph in generated text | Within 30% of profile | | Punctuation patterns | Count em-dashes, semicolons per sentence | Within 50% of profile |

Step 5: Refine if Needed

If any metric exceeds acceptable deviation:

Identify the specific constraint that was violated
Strengthen that constraint in the system prompt
Regenerate the content
Re-compare

Maximum 3 refinement iterations. After 3 iterations, deliver the best result with a deviation report.

Step 6: Deliver with Quality Report

Provide the generated content along with a quality summary:

## Voice Clone Quality Report

**Profile Used:** user-style-profile.json (N samples, M total words)

| Metric              | Target | Actual | Status |
| ------------------- | ------ | ------ | ------ |
| Avg sentence length | 18.3   | 17.8   | PASS   |
| Vocabulary overlap  | >= 40% | 45%    | PASS   |
| Tone formality      | 2.8    | 3.1    | PASS   |
| Paragraph length    | 3.2    | 3.5    | PASS   |
| Refinement rounds   | -      | 1      | -      |

Iron Laws

ALWAYS load the style profile before generating any content -- generation without profile constraints produces generic output that does not match the target voice.
NEVER copy verbatim sentences or distinctive phrases from the original samples into generated content -- the goal is to replicate patterns, not plagiarize.
ALWAYS perform A/B comparison after generation -- unverified output may drift significantly from the target voice without detection.
NEVER exceed 3 refinement iterations -- diminishing returns beyond 3 rounds; deliver the best result with a deviation report instead.
ALWAYS include a quality report with the delivered content -- the consumer needs to know how closely the output matches the target voice.

Anti-Patterns

| Anti-Pattern | Why It Fails | Correct Approach | | ------------------------------------------------------- | ----------------------------------------------------- | -------------------------------------------------------- | | Generating without loading the style profile | No constraints; output is generic | Always load and validate profile before generation | | Hardcoding style constraints instead of reading profile | Constraints become stale; do not match actual samples | Read from .claude/context/data/user-style-profile.json | | Copying memorable phrases from samples | Plagiarism detection; not authentic style transfer | Extract patterns (word frequency, tone) not content | | Skipping the comparison step | No quality signal; style drift goes undetected | Always run A/B comparison on all five metrics | | Infinite refinement loop | Diminishing returns; wastes tokens and time | Cap at 3 iterations; deliver best result with report |

Integration with style-analyzer

This skill depends on style-analyzer for its input:

[User samples] --> style-analyzer --> user-style-profile.json --> voice-clone-generator --> [Generated content]

If user-style-profile.json does not exist when this skill is invoked, the agent should invoke style-analyzer first.

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 "voice clone generation style constraints"

Read .claude/context/memory/learnings.md

After completing:

New generation pattern -> .claude/context/memory/learnings.md
Quality issue found -> .claude/context/memory/issues.md
Constraint tuning decision -> .claude/context/memory/decisions.md

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

Agent Skills: Voice Clone Generator

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