brand-voice
Acerca de
La habilidad `brand-voice` define y documenta un sistema completo de voz y tono de marca, incluyendo atributos, vocabulario y cambios de tono contextuales. Se activa para crear pautas de voz, auditar la coherencia de los textos o capacitar a equipos/IA sobre la personalidad de la marca. Úsala para establecer el sistema de voz en sí, mientras que la habilidad `editorial-qa` es para verificar borradores específicos frente a pautas ya existentes.
Instalación rápida
Claude Code
Recomendadonpx skills add rampstackco/claude-skills -a claude-code/plugin add https://github.com/rampstackco/claude-skillsgit clone https://github.com/rampstackco/claude-skills.git ~/.claude/skills/brand-voiceCopia y pega este comando en Claude Code para instalar esta habilidad
Documentación
Brand Voice
Define how a brand sounds in writing. Document it in a way that anyone (writer, designer, founder, AI assistant) can apply it consistently.
This skill produces a standalone voice document that can either live in the brand style guide or feed into it.
When to use
- Defining brand voice for the first time
- Auditing existing copy for voice consistency
- Training a team, freelancer, or AI assistant on the brand voice
- Refining a voice that exists but is inconsistent across applications
- Adapting voice when a brand evolves (new audience, new positioning)
When NOT to use
- Writing specific copy (use
content-and-copy) - Brand visual identity work (use
brand-identity) - Documenting a complete brand system (use
brand-style-guide, which incorporates voice) - Initial brand exploration (use
brand-ideation)
Required inputs
- The brand and its positioning
- The audience and what they need to hear
- 5 to 10 examples of existing brand writing (if any)
- 3 to 5 reference brands whose voice resonates with where this brand should go
- Any voice attributes already specified in the brief
The framework: 4 layers
Voice has four layers, stacked. Each layer constrains the one below it.
Layer 1: Voice attributes
The constants. The personality traits that define how the brand sounds across every context.
Pick 3 to 5 attributes. Pair each with what it is NOT (the failure mode if overdone).
Common attribute pairings (NOT a menu - generate your own):
- Confident, not arrogant
- Direct, not blunt
- Warm, not saccharine
- Witty, not sarcastic
- Smart, not academic
- Honest, not harsh
- Playful, not silly
- Practical, not boring
- Bold, not loud
- Curious, not unfocused
The "not" half is what saves writers from overshooting. "Confident" alone produces swagger. "Confident, not arrogant" tells writers where the line is.
Layer 2: Tone shifts
Voice is constant. Tone adapts to context.
Map the major contexts the brand writes in. For each, document how voice expresses differently.
Common contexts:
| Context | Tone shift |
|---|---|
| Onboarding | Warmer, more enthusiastic, slightly slower pace |
| Hero / marketing | Confident, signature voice fully on |
| Product copy / UX | Direct, helpful, brief |
| Error messages | Calm, matter-of-fact, no apology theater |
| Success states | Brief celebration, redirect to next action |
| Empty states | Helpful, slightly playful, suggest action |
| 404 / not found | Self-aware, light, points the way home |
| Account deletion / cancellation | Quiet, respectful, no jokes |
| Pricing | Direct, transparent, confidence-inspiring |
| Legal / TOS | Plain language version sits next to the legal version |
| Support / help center | Patient, thorough, no condescension |
| Crisis communication | Calm, factual, accountable |
| Product announcements | Excited but not breathless |
| Email subject lines | Specific, never click-bait |
Voice stays consistent across all of these. Tone is what shifts.
Layer 3: Vocabulary and grammar
The granular dial settings.
Vocabulary preferences:
- Words and phrases the brand uses
- Words and phrases the brand avoids
- Words the brand has redefined (if any) - e.g., a SaaS product calling its features "huddles" instead of "meetings"
- Industry jargon: keep it (signals expertise) or strip it (signals approachability)
- Sentence-opening preferences (some brands lean on imperatives, some on questions, some on statements)
Grammar and style:
- Contractions (use them = casual, avoid them = formal)
- Sentence length default (short = punchy, medium = considered, long = literary)
- Punctuation marks favored or avoided (em dash is famously polarizing)
- Pronouns ("we" / "you" / "I")
- Capitalization style (title case, sentence case, all-lowercase deliberate)
- Number formatting (spell out under ten, or always digits)
- Oxford comma (use or skip)
- Active vs passive voice (most brands prefer active)
Layer 4: Examples and patterns
Voice is taught through examples, not rules. Build a library.
For each major content type, show:
- Bad example (off-voice)
- Good example (on-voice)
- Brief note on what changed
Cover the content types listed in the paired examples library of references/voice-document-template.md. The template owns that list, so there is one list to maintain rather than two that drift apart.
The floor is 15 paired examples. One pair per type in the template clears it. Past 25 the library gets harder to scan than to use, so treat 25 as the practical ceiling, not a hard cap. This is the most-used part of the voice doc in practice.
Workflow
- Pick the entry point.
references/voice-frameworks.mdhas a "Choosing a framework" section that decides this. A brand starting from zero begins with archetypes there, then dimensions, then attributes, and rejoins this workflow at step 2. Everything below assumes copy exists to audit. - Audit existing copy if it exists. Identify what is on-brand, what is off, what patterns recur.
- Layer 1: Voice attributes. Generate 5 to 8 candidates with "we are X, not Y" framing. Pick 3 to 5.
- Layer 2: Tone shifts. List 8 to 15 contexts the brand writes in. Note the tone shift for each.
- Layer 3: Vocabulary and grammar. Define preferences. Skip default rules unless they actually distinguish the brand.
- Layer 4: Examples. Build the paired-example library against the template's content-type list, to the floor stated in the Layer 4 section above.
- Stress-test. Pick a fresh writing brief and apply the voice doc. Score the result with the stress test in
references/voice-frameworks.md: 1 to 5 against each attribute, and anything scoring below 3 on any attribute is off-voice. Below 3 means the doc is incomplete, not that the copy needs another pass. - Document. Use the template in
references/voice-document-template.md. - Distribute. Voice docs only work if they get used. Make the doc easy to reference inline (link to it from CMS templates, brief templates, AI assistant prompts).
Failure patterns
- Generic attributes ("friendly, professional, approachable"). Every brand says this. Means nothing. Pick attributes that genuinely distinguish.
- No "we are NOT" pairings. Without the rejection, attributes drift toward extremes.
- Voice doc with no examples. Rules without examples cannot be applied.
- Examples that are obviously bad and obviously good. Real voice work shows nuanced shifts, not cartoonish before/after.
- Skipping tone shifts. Treating voice as one-size-fits-all leads to a brand that sounds wrong in error states or legal contexts.
- Documenting aspirational voice. If the brand does not actually sound this way today and has no plan to shift, the doc is fiction.
- Voice without distribution. A perfect doc that no one references is worth nothing.
Output format
Default output is a markdown document at voice.md in the brand folder. Sections:
- Voice attributes (with we-are-not pairings)
- Tone shifts by context
- Vocabulary preferences
- Grammar and style rules
- Paired examples library (the most-used section)
- Anti-patterns (specific phrases or constructions to avoid)
- References (the brands and writers we are inspired by)
This doc can stand alone or feed into brand-style-guide.
Reference files
references/voice-document-template.md- Fillable template.references/voice-frameworks.md- Detailed walkthrough of the Nielsen Norman 4 dimensions, Jung archetypes, and the "we are X not Y" approach.
Repositorio GitHub
Preguntas frecuentes
¿Qué es el Skill brand-voice?
brand-voice es un Skill de Claude creado por rampstackco. Los Skills agrupan instrucciones y recursos que Claude carga cuando los necesita para realizar tareas relacionadas con brand-voice sin indicaciones adicionales.
¿Cómo instalo brand-voice?
Usa los comandos de instalación de esta página: añade brand-voice a Claude Code como plugin o clona su repositorio en tu directorio de skills y reinicia Claude para cargarlo.
¿A qué categoría pertenece brand-voice?
brand-voice pertenece a la categoría Meta.
¿Se puede usar brand-voice gratis?
Sí. brand-voice aparece en AIMCP y se puede instalar gratis.
Habilidades relacionadas
Esta habilidad proporciona una configuración probada en producción para Content Collections, una herramienta centrada en TypeScript que transforma archivos Markdown/MDX en colecciones de datos con tipado seguro mediante validación Zod. Úsala al construir blogs, sitios de documentación o aplicaciones Vite + React con mucho contenido para garantizar seguridad de tipos y validación automática de contenido. Abarca todo, desde la configuración del plugin de Vite y compilación MDX hasta la optimización de despliegue y validación de esquemas.
Esta habilidad permite a los desarrolladores crear aplicaciones con la plataforma de mercados de predicción Polymarket, incluyendo la integración de API para operaciones y datos de mercado. También proporciona transmisión de datos en tiempo real a través de WebSocket para monitorear operaciones en vivo y actividad del mercado. Úsela para implementar estrategias de trading o crear herramientas que procesen actualizaciones de mercado en tiempo real.
Esta habilidad ayuda a los desarrolladores a crear complementos de OpenCode que se conectan a más de 25 tipos de eventos, como comandos, archivos y operaciones LSP. Proporciona la estructura del complemento, las especificaciones de la API de eventos y los patrones de implementación para módulos en JavaScript/TypeScript. Úsala cuando necesites interceptar, monitorear o extender el ciclo de vida del asistente de IA de OpenCode con lógica personalizada basada en eventos.
SGLang es un framework de alto rendimiento para el servicio de LLM que se especializa en generación rápida y estructurada para JSON, expresiones regulares y flujos de trabajo de agentes utilizando su caché de prefijos RadixAttention. Ofrece una inferencia significativamente más rápida, especialmente para tareas con prefijos repetidos, lo que lo hace ideal para salidas complejas y estructuradas, y conversaciones multiturno. Elige SGLang sobre alternativas como vLLM cuando necesites decodificación restringida o estés construyendo aplicaciones con uso extensivo de prefijos compartidos.
