SCHEMA.md

Brand Voice Spec — schema

voice.json is the canonical artifact. Everything in data/ is derived from it. The format is small on purpose: enough structure for a model to load and enforce, not so much that a human won't maintain it.

Top level

Field	Type	What it is
brand	string	The brand this voice belongs to.
status	string	sample for this reference; live for a real brand.
revision	int	Bumped every time a rule changes. Pairs with changelog.
registers	object	Named voices within one brand. Here: cafe (guest-facing) and trade (wholesale). A field can carry a register to say which voice it belongs to.
tone	string[]	Adjectives. The 30,000-foot read.
guidelines	object[]	The rules. See below.
doAndDont	object[]	Genuinely paired on-voice / off-voice examples.
surfaceDoDont	object[]	Per-surface guidance plus independent pools of good and bad examples.
promptKit	object[]	Reusable generation prompts, one per recurring writing job.
bannedTerms	string[]	Hard prohibitions.
preferredTerms	string[]	Reach-for-these words.
vocabulary	object	Concrete nouns grouped by domain.
signaturePhrases	string[]	Lines that are load-bearing for the brand.
changelog	object[]	One entry per revision. Each links to the correction that caused it.
voiceQueue	object[]	Captured corrections, adopted or pending. The input side of the loop.

guidelines[]

{
  "rule": "Talk like a regular, not a sommelier.",
  "derivedFrom": "Unpretentious",
  "explanation": "Tasting notes help people choose. Tasting poetry makes them feel dumb.",
  "derivedFromCorrection": "vn_0002"
}

derivedFromCorrection is the join to voiceQueue. It answers "why does this rule exist?" with a real correction, not a committee.

The loop

This is the part that matters. Most brand voice is written once and rots. This schema treats voice as a living artifact:

1. A draft comes back wrong. Someone (a human or an agent) files a note in voiceQueue.
2. During a periodic sweep, adopted notes become or amend guidelines.
3. revision bumps and a changelog entry records the change, linked back to the note.
4. The next generation reads the updated spec. The mistake does not repeat.

data/changelog.jsonl joins each revision to the correction that produced it, so you can read the voice's whole evolution as cause and effect.

Using it with a model

• Put guidelines + bannedTerms + a few preference_pairs in the system prompt to steer generation.
• Pick the right register for the surface you're writing.
• Use promptKit entries as parameterized templates for recurring jobs.
• Score output against bannedTerms and the rules as a cheap voice-lint.