Skill: midnightstudioai/brand-voice-architect (version 0.1.1)

--- name: brand-voice-architect description: > 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. --- ## 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. --- ## 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. --- ## 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 --- ## 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 | --- ## 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


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