# Brand Voice > Use when defining how a brand SOUNDS as a reusable system: adjectives turned into linguistic rules, four tone dimensions as ratios, a use/avoid word bank, an AI voice-DNA block — so content stops sounding like five different writers. NOT the finished copy written against it (that is `landing-copy` / `marketing`), NOT the brand's look (`brand-identity`). - Skill: `ericrisco/brand-voice` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add ericrisco/brand-voice` - Raw SKILL.md: https://api.skillmd.com/api/skills/ericrisco/brand-voice/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: ericrisco (https://skillmd.com/u/ericrisco) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ericrisco/brand-voice --- # Brand Voice — How the Brand Sounds You own the **reusable voice-and-tone system**: 3–5 personality traits → concrete linguistic rules → a position on the four tone dimensions → a use/avoid word bank → a tone-by-context matrix → a paste-into-the-prompt voice-DNA block. The output is a persisted document, never a finished piece of copy. The line: **brand-voice owns the reusable definition of how the brand sounds.** The moment you write one finished piece against it, that is a copywriting skill — page hero/value prop/CTA is `landing-copy`; launch emails and channel posts are [`../marketing/SKILL.md`](../marketing/SKILL.md); blog and article systems are `content-engine`, `article-writing`, `newsletter`, `social-publisher`; an investor narrative is [`../pitch-deck/SKILL.md`](../pitch-deck/SKILL.md); a live customer ticket is `customer-support` (it consumes this guide, it does not author it). The way the brand *looks* — logo, color, type, design tokens — is `brand-identity`, and layout/motion is [`../design/SKILL.md`](../design/SKILL.md). ## Voice vs. tone (the load-bearing distinction) **Voice is constant; tone flexes by context.** Voice is the brand's fixed personality across everything it writes. Tone is the local adjustment for the reader's emotional state and the topic's sensitivity. A frustrated user does not want a joke; a celebration screen does not read like a financial disclosure — yet both are the same voice. (Nielsen Norman Group, "The Four Dimensions of Tone of Voice," pub. 2016-07-17, updated 2023-08-16.) Why it matters: you author **one** voice and apply **many** tones. Conflate them and you get a guide that says "be playful" on a fraud-alert page — unusable. The guide locks voice once and tabulates tone per context (Step 5). Why bother at all: consistent brand presentation correlates with revenue uplift — ~23% average, up to ~33% at the upper range across 1,800 brands in 14 industries (Lucidpress/Marq, "State of Brand Consistency"). Treat it as calibration for the effort, not a causal promise — it is a correlational study. ## The flow (six steps) ```text 1 Traits (3–5) → 2 Rules (Bad→Good) → 3 Four dimensions (ratios) → 4 Word bank (use/ban) → 5 Tone-by-context matrix → 6 AI voice-DNA block → persist + audit ``` ### Step 1 — Traits (pick 3–5) Distill the brand to **3–5 adjectives**. Fewer than 3 is not a personality; more than 5 is unmemorable and nobody applies them. (Sprout Social brand-voice guide; Inkbot Design brand-voice chart.) Reject brand-neutral adjectives. If a competitor would never claim the *opposite*, the word is filler and says nothing. "Innovative," "passionate," "customer-focused," "cutting-edge" — no brand claims "stagnant" or "indifferent," so these traits exclude nothing. Each trait gets a one-line **this means / this does not mean**, so it is testable: ```text Plain-spoken this means: we say "we fixed it" — short Anglo-Saxon words, no hedging this does NOT mean: dumbed-down or curt; we still explain the why Quietly confident this means: we state the benefit and stop; no exclamation marks this does NOT mean: arrogant, or making claims we can't back with proof ``` ### Step 2 — Rules (each trait → 2–3 linguistic rules) Vague directives fail, especially for an LLM: "be professional" produces nothing reproducible. Voice only transfers when quantified into linguistic rules. (Search Engine Land, "How to train in-house LLMs on brand voice," 2025; Fishtank, "Train Generative AI to Speak in Your Brand Voice," 2025.) Per trait, write 2–3 rules across these levers — **person, sentence-length ceiling, active vs. passive, contractions, jargon policy** — and show one Bad→Good rewrite per cluster: ```text Trait: Plain-spoken R1 Active voice. Subject does the verb. R2 Sentence ceiling ~20 words; break anything longer. R3 Jargon only when defined in-line on first use. Bad : "Optimal outcomes are facilitated through the leveraging of our platform's robust capabilities." (passive, 12-word abstraction, banned words) Good: "Our platform does the heavy lifting so your team ships faster." ``` ```text Trait: Quietly confident R1 First person plural ("we"), second person for the reader ("you"). R2 Use contractions ("we're", "you'll") — formal-but-human, not stiff. R3 Zero exclamation marks; the claim carries the energy. Bad : "We are SO excited to announce our amazing new feature!!!" Good: "New: branch previews ship with every PR. No config." ``` ### Step 3 — Plot the four dimensions (decision table) Tone of voice is measurable on four sliding scales, not switches. (Nielsen Norman Group, same article.) Pick a position on each as a **ratio**, not "somewhere in the middle" — a ratio forces a defensible choice. (Sprinklr / Bigeye brand-voice frameworks, 2025.) A financial brand might run 80/20 formal; a fitness app 30/70 serious-vs-playful. This branches per brand, so the table earns its place: | Dimension | Position (ratio) | Why this brand sits here | |---|---|---| | Formal ↔ Casual | 65 / 35 casual | Buyers are technical and busy; warmth without slang. | | Serious ↔ Funny | 80 / 20 serious | We handle money/data; humor only in low-stakes moments. | | Respectful ↔ Irreverent | 70 / 30 respectful | We challenge category clichés, never the reader. | | Matter-of-fact ↔ Enthusiastic | 60 / 40 matter-of-fact | Proof over hype; energy lives in verbs, not adjectives. | Fill the ratios from the traits, not from taste. If a ratio contradicts a trait, one of them is wrong — reconcile before moving on. ### Step 4 — Word bank Two lists. Power words and a ban list. (Oxford College of Marketing, "AI Brand Voice Guidelines," 2025-08-04.) - **Power words (15–20):** the vocabulary the brand leans on, derived from the traits. "Plain-spoken + confident" → ship, fix, build, fast, clear, done, plain, real, works. Not a thesaurus dump — words a human would recognize as *this* brand. - **Ban list (the drift killer):** corporate filler and AI tells. This list is what stops off-brand drift and the generated-by-a-bot smell. Starter set: `leverage`, `seamless`, `elevate`, `delve`, `robust`, `unlock`, `game-changer`, `in today's fast-paced world`, `revolutionize`, `synergy`, `cutting-edge`, `best-in-class`. Add brand-specific bans (e.g. never say "users," say "teams"). The full starter ban list and the method for deriving power words from traits live in [`references/word-bank.md`](references/word-bank.md). ### Step 5 — Tone-by-context matrix Voice stays fixed (the row content proves it); tone shifts per context. Build the matrix so writers and the LLM know which dial to turn where: | Context | Voice (constant) | Tone shift | Example line | |---|---|---|---| | Onboarding | plain-spoken, confident | warm, encouraging | "You're in. Let's connect your first repo." | | Error / failure | plain-spoken, confident | plain, reassuring, zero humor | "That upload failed. Your data is safe — try again." | | Success / celebration | plain-spoken, confident | a little warmth, still no hype | "Done. Your preview is live." | | Billing / account | plain-spoken, confident | precise, calm, no jokes | "Your plan renews June 30. Cancel anytime, no fees." | | Legal / security notice | plain-spoken, confident | formal, exact, literal | "We encrypt data in transit and at rest. See our DPA." | The voice column never changes line to line — that is the whole point. Only the tone column moves. ### Step 6 — The AI voice-DNA block Assemble the guide into one paste-into-a-system-prompt block so an LLM (or any writer) reproduces the brand. Concrete rules + lexicon, never adjectives alone: ```text VOICE DNA — Traits: plain-spoken, quietly confident, technical-but-human. Rules: active voice; sentences <=20 words; use contractions; first person plural "we", reader as "you"; no exclamation marks; jargon only if defined. Dimensions: 65/35 casual, 80/20 serious, 70/30 respectful, 60/40 matter-of-fact. Use: ship, fix, build, fast, clear, real, works, plain. Never use: leverage, seamless, elevate, delve, robust, unlock, game-changer, "in today's fast-paced world", revolutionize, synergy, best-in-class. Tone by context: onboarding=warm; error=plain+reassuring, no humor; success=light warmth, no hype; billing=precise+calm; legal=formal+exact. ``` **Persist it.** Write the compiled guide under `02-DOCS/wiki/brand/voice-guide.md` and the voice-DNA block beside it, per the `harness` Karpathy-wiki convention (compiled brand articles under `02-DOCS/wiki/brand/`, raw user inputs under `02-DOCS/raw/brand/`). The persisted file is an OKF v0.1 wiki article: open it with YAML frontmatter carrying a non-empty `type:` (use `type: brand-voice`) — see the frontmatter block in [`references/voice-guide-template.md`](references/voice-guide-template.md), which is also the fill-in-the-blanks skeleton for the whole guide (traits → rules → 4-D ratios → word bank → context matrix → voice-DNA block) with one fully worked mini-example brand. This is the exact study `marketing`, `landing-copy`, and `content-engine` read to ground their copy. A guide in a slide deck is invisible to them. ## Auditing for drift To score a sample against the guide, run three passes: 1. **Ban scan** — does the sample use any banned word? Each hit is a drift point. 2. **Rule check** — passive voice, sentences over the ceiling, exclamation marks, undefined jargon. Count violations. 3. **Trait test** — read it cold: which traits surface? If "plain-spoken + confident" reads as "hypey + vague," it is off-brand regardless of word count. Off-brand reads like everyone else: abstract nouns, hedged claims, AI tells, energy faked with punctuation instead of verbs. The fix is always a rewrite toward a rule, never "make it pop." ## Anti-patterns | Anti-pattern | Why it fails | Do this instead | |---|---|---| | Traits = "innovative, passionate, customer-focused" | No competitor claims the opposite; excludes nothing | Pick traits a rival would reject; add this-means/this-does-not-mean | | "Be professional" as the only guidance | An LLM and a junior writer can't reproduce an adjective | Quantify into rules (person, length, voice, jargon) + a Bad→Good | | Tone "somewhere in the middle" on every axis | Vague middle = no decision = generic output | Commit to a ratio (80/20) and justify it from a trait | | Voice changes per channel | Channel-by-channel voices = no recognizable brand | Voice fixed; tone flexes per context (Step 5) | | No ban list | Drift and AI tells creep in unchecked | The ban list is the drift killer — ship it first | | Guide lives in a deck or someone's head | Downstream skills and LLMs can't read it | Persist machine-readable under `02-DOCS/wiki/brand/` | | Writing the actual landing/email/article | That is a finished piece, not the definition | Stop; hand to `landing-copy` / `marketing` / `content-engine` | ## Verify `scripts/verify.sh ` is a read-only structural linter for a produced voice guide: it checks the required sections are present (traits, rules with Bad→Good, four-dimension ratios, a non-empty ban list, context matrix, voice-DNA block), flags a trait count outside 3–5, warns on brand-neutral filler used as a trait, and greps the guide's own prose for words it lists in its own ban list (self-consistency). Empty or clean input exits 0 — no false failure.