Brand Voice Architect

A high-precision engine for deconstructing, documenting, and synthesizing brand-specific linguistic patterns and tonal architectures. Use this skill whenever a user wants to: create or generate a brand voice guide, analyze writing samples or a corpus for tone/style, review content for linguistic consistency, build a voice system prompt, define brand pillars, identify prohibited words or preferred vocabulary, create "this not that" style guides, adapt tone across platforms (LinkedIn vs. technical docs vs. social), or reverse-engineer competitor or reference brand voices. Trigger even for loosely related requests like "make our writing more consistent", "what tone should we use?", or "analyze how we write."

Brand Voice Architect (BVA)

A skill for engineering, documenting, and synthesizing brand-specific voice with quantifiable precision. Brand voice is treated as a Linguistic DNA — a measurable baseline, not an aesthetic preference.

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Core Workflow

Phase I: Decomposition — /analyze [corpus]

Run a linguistic audit on provided text samples:

1. Lexical Audit — High-frequency verbs/adjectives, prohibited terms, vocabulary signature
2. Structural Mapping — Average Sentence Length (ASL), syntactic complexity, variance
3. Sentiment Baseline — Emotional temperature on a 0.0–1.0 scale

→ Use scripts/voice_analyzer.py to compute metrics programmatically when a corpus is provided.

Phase II: Architectural Design — /synthesize [pillars]

Build the voice matrix:

1. Pillar Definition — Establish 3 core attributes (e.g., Authoritative, Wit-driven, Technical)
2. The Spectrum — Define "This, Not That" logic gates for each pillar
3. Persona Encoding — Translate pillars into LLM system-level instructions

→ Use scripts/prompt_synthesizer.py to generate deployable system prompts.

Phase III: Delivery

1. Artifact Generation — Produce voice guide docs, style reference cards, prompt templates
2. Manual Review — /review [output] provides a qualitative checklist to assess whether output aligns with the established voice pillars (Claude-assisted, not script-automated)
3. Platform Pivot — /pivot [context] adapts voice for specific channels while preserving DNA, using generate_platform_pivot() from prompt_synthesizer.py

Note on prohibited words: The generated system prompt instructs the LLM to replace prohibited words with preferred equivalents. This is a prompt-level instruction — enforcement depends on the model following the system prompt, not on automated script-level filtering.

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The 4-Pillar Framework

Map every brand voice across four axes to define its Safe Operating Area:

Axis	Poles
Character	Friendly ←→ Authoritative
Tone	Humorous ←→ Serious
Language	Simple ←→ Complex
Purpose	Helpful ←→ Entertaining

See references/methodology.md for full framework details including Cadence Analysis and Semantic Salience scoring.

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Mandatory Output Components

Every Brand Voice engagement must produce:

1. Metrics Report — Lexical density %, ASL, top keywords, cadence variance
2. Voice Matrix — 3 pillars × "This/Not That" for each
3. System Prompt — Ready-to-deploy LLM persona encoding
4. Platform Pivots — At minimum: formal/informal, long-form/short-form variants
5. Prohibited/Preferred Lexicon — Concrete word lists

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Quick Reference Commands

Command	Action	Implementation
/analyze [corpus]	Linguistic audit on provided text	scripts/voice_analyzer.py
/synthesize [pillars]	Generate LLM system prompt from pillars	scripts/prompt_synthesizer.py
/review [output]	Qualitative checklist review against voice pillars	Claude-assisted (no script)
/pivot [context]	Adapt voice for target platform/audience	generate_platform_pivot() in prompt_synthesizer

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Scripts

• scripts/voice_analyzer.py — Computes lexical density, ASL, cadence variance, sentiment temperature, and top keywords from a corpus
• scripts/prompt_synthesizer.py — Generates deployable LLM system prompts from a BrandConfig object; includes generate_platform_pivot() for channel-specific adaptations

References

• references/methodology.md — Full technical methodology: 4-Pillar Framework, Cadence Analysis, Semantic Salience, Human-AI Collaborative Loop