Brand Voice logo

Brand Voice

Organization
ssheleg
brand-voice

Use when defining or checking how a product speaks: tone of voice, terminology, canonical facts, register. Triggers: "tone of voice" / "тон оф войс", "brand voice" / "голос бренда", "брендбук", "наша терминология". Not for writing the text (copywriting). Refusal phrase "no brand" / «без бренда».

Overview

Publisherssheleg
Repositorysuper-ux
Skill namebrand-voice
Stars
6
Forks
0
Bundled files
20
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 20 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ssheleg on GitHub. Read the source before you install it.

Installation

Install the Brand Voice AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ssheleg/super-ux/tree/main/plugins/super-ux/skills/brand-voice
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ssheleg/super-ux.git /tmp/super-ux
mkdir -p .claude/skills
cp -r /tmp/super-ux/plugins/super-ux/skills/brand-voice .claude/skills/brand-voice
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Brand Voice in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Brand Voice on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Brand Voice is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

brand-voice — define and hold the verbal identity

Part of super-ux — see system-map.md for the whole pipeline. After changes, run python3 docs/brand/lint.py.

docs/ux/ decides what the product does. This skill decides how it speaks, and owns docs/brand/ in the target project.

Format contract: brand-contract.md (brand-contract v1). Read it before writing or editing anything under docs/brand/. Never deviate from its file names, field names or statuses — the linter and the audit key off them.

The pack library: voice-packs.md — six archetypes, each with a declared failure mode. The register model: surface-registers.md. Locales: localization.md.

Triggers

The phrases that route to this skill, in English and Russian. The description keeps only the strongest of them so the skill listing stays inside its budget (2026-10-20); the family routing hook matches this list.

"tone of voice" / "тон оф войс", "brand voice" / "голос бренда", "brandbook" / "брендбук", "our terminology" / "наша терминология", "how should this sound" / "как это должно звучать", "the copy is inconsistent" / "текстовка разная везде", starting any marketing surface, onboarding a product into docs/brand/.

Invoked with no task → status, then one next action

  1. Report the state: is a pack recorded, what voice.md says its Status is, how many facts carry ⚠ TBD or no source, locale parity, and the linter's error and warning counts.
  2. Name every unresolved fact. Never invent one to close a gap — an unknown is reported, not filled.
  3. Propose exactly one next action, usually the top open fact or the surface the user actually came for.

Where the voice comes from

The pack is derived from docs/ux/foundation.md — personas, jobs, and the deciding input: what the user loses when the product fails. A voice that charms when the stakes are a playlist is intolerable when the stakes are payroll.

The dependency runs one way. A persona never changes because a tone was appealing. With no foundation, work in degraded mode: stamp Derived-from: inferred, say plainly that the WHY layer should be built, and continue rather than blocking.

The route itself has a spoken opt-out: an operator saying "no brand" / «без бренда» declines it — proceed without the pack and say so, rather than dropping it silently.

Choosing a workflow

SituationWorkflow
No docs/brand/Init
Pack chosen, product specifics missingCalibrate
Positioning, personas or facts changedUpdate
Consistency questioned, or before a launchValidate

Announce which one you are running.

Init

  1. Read the foundation. Personas, JTBD, journeys, stakes. Where it is absent, interview: who is this for, what are they accountable for, what does failure cost them.
  2. Read what already exists. Interface strings, landing copy, README, store listing, recent posts. The current voice is data even when nobody chose it.
  3. Propose one pack with reasoning, plus one alternative. Name why the runner-up loses. Let the user pick.
  4. Seed docs/brand/ from this skill's own templates/brand/, and fill the Sources: block first — nothing else can be checked until the linter knows where the project keeps its text.
  5. Inventory sweep for strings.md: routes, screens, buttons, states, errors, empty states. Every string gets a row with its file:line and the scenario it serves. For a large codebase, use available read-only subagents in parallel, one area each; without delegation, inspect the same areas sequentially and keep the same evidence requirements.
  6. Ask which humanization pass this project wants, and write the answer to voice.md's Humanization pass: field. own is the default and the only one that reads this pack's registers and facts; npx sshlg-skills humanizers shows what else is installed and how to install what is not. Ask once — the field exists so nobody is asked twice, and it selects a pass, never a verdict.
  7. Everything starts Status: draft.

Calibrate

The pack is a starting position; this is where it becomes the product's own.

  • Terminology. Which generic words does this product replace, and with what. Seed the banned table from weak verbs, hedging chains and ai-tells.md, then add what is specific here.
  • Facts. Every number the product quotes, with a source, a checked date and a review date. Mark internal figures Public: no.
  • Channels. One record per surface the product actually has. Register as deltas; Forbidden: always carries both the physics and the brand half.
  • Ready lines. Six to ten, written for this product, not copied from the pack.
  • Failure mode. Copy the pack's into voice.md so the audit can look for it by name.
  • The em dash invariant. Seed voice.md's invariants with "no em dash (U+2014) in new text, in any language" (owner decision, 2026-10-18). It is not a voice choice a pack can relax: every pack inherits it, and only the grammatical Russian dash where no rewrite reads naturally survives (ai-tells.md AT-06, B062, B067).

Present section by section for approval. Approved moves draft → validated.

Update

  1. Identify what changed — positioning, a persona, a price, a tier name, a new surface.
  2. Apply the edits. A changed entity name propagates to terminology.md, strings.md and every locale in the same change.
  3. Changed files drop to Status: draft until re-approved.
  4. Re-stamp Last calibrated.

Validate

  1. Integrity — every field the contract requires is present; statuses are legal; Derived-from resolves against the foundation.
  2. Coverage — every surface the product ships has a record; every locale declared has a file; every quoted number has a fact.
  3. Consistency — run python3 docs/brand/lint.py and fold its findings in. It proves the mechanical half; report what it cannot see as questions for /ux-audit copy.
  4. Staleness — foundation newer than the last calibration, facts past review, physics rules past their checked date.

Definition of done

  • Contract honored; the marker on every file.
  • The user has seen and approved new or changed sections.
  • python3 docs/brand/lint.py exits 0, or every remaining finding is reported with a reason.
  • Unknowns stated as unknowns. Nothing invented to make a table look full.

What else is in references/. This skill names 6 contract(s) directly; references/ holds 13, because a contract this skill names links others and the whole closure ships inside the skill — the skills CLI installs one directory and a sibling's file would arrive dangling. Open the ones named here; the rest are reached from them, by name, when a contract sends you.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Brand Voice AI skill do?

Use when defining or checking how a product speaks: tone of voice, terminology, canonical facts, register. Triggers: "tone of voice" / "тон оф войс", "brand voice" / "голос бренда", "брендбук", "наша терминология". Not for writing the text (copywriting). Refusal phrase "no brand" / «без бренда».

Why use Brand Voice on TypingMind?

Because you install it once and use it with any model. Brand Voice is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Brand Voice in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ssheleg/super-ux/tree/main/plugins/super-ux/skills/brand-voice. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Brand Voice?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Brand Voice?

As many as you like. As long as a model supports skills, you can use Brand Voice with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Brand Voice AI skill free?

Yes. It is published on GitHub by ssheleg under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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