brand-voice

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UpdatedJuly 16, 2026 at 17:57

Derive a founder or company voice profile from real writing samples found in connected sources, then use it to ensure all content sounds like the actual person — not generic AI copy. Use this skill whenever the user asks about their writing style, brand voice, tone of voice, how they sound, or wants any content to match their voice. Also trigger when another skill needs a voice profile before writing copy, posts, emails, or outreach.

Installation

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

Source

Unabyss

Unabyss/unabyss-skills

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Related occupationsSOC

Based on SOC occupation classification

Market Research Analysts and Marketing SpecialistsBusiness and Financial Operations Occupations·SOC 13-1161

SKILL.md

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name

brand-voice

description

Brand Voice

Extract a reusable voice profile from the founder's actual writing — so every piece of content sounds like them, not like a language model approximating them.

Required Integrations

This skill uses Unabyss MCP — agentic_query to surface real writing samples across all connected sources.

If Unabyss is not connected, tell the user: "This skill requires Unabyss MCP. You can connect it from the Tools menu."

How to Run

Step 1 — Check for an existing export

Call export_list first. If a brand voice or voice profile export already exists and is fresh, call export_read — no need to regenerate.

Step 2 — Pull writing samples with `agentic_query`

Samples are scattered across emails, posts, docs, Slack — use agentic_query:

Find examples of my own writing from across all connected sources — emails I've sent, LinkedIn or X posts, Slack messages where I'm explaining something, docs or notes I've written, launch announcements, product updates, or any other text where I'm clearly the author. Prioritize recent material. Return the actual text, not summaries.

Call agentic_query_read to retrieve results. Aim for 8–12 distinct samples across different contexts.

Step 3 — Analyze and produce the profile

VOICE PROFILE — [Name / Company] Generated from [N] samples

In one sentence How this person writes, in plain language.

Tone 3–5 specific descriptors, each with a concrete example from the actual samples.

Sentence style Typical length, structure, how they open and close ideas.

How they make claims Direct / hedged / evidence-first / assertion-first — with examples.

What they reach for Numbers, analogies, product examples, customer stories.

Vocabulary Words and phrases that appear naturally. Words and constructions they never use.

What to avoid Specific patterns, phrases, or tones that would immediately sound wrong.

3 on-voice examples Pulled verbatim from samples, with a one-line note on why each works.

Step 4 — Save the profile

Once generated, call export_create_from_text to save the voice profile as an export — so other skills can load it instantly next time with export_read instead of regenerating.

Output Rules

Always check export_list before running agentic_query — voice profiles change slowly
Every observation grounded in an actual sample — no invented characterizations
If samples are thin, note which sources seem missing and produce a partial profile with caveats
No preamble — start directly with the voice profile

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name

brand-voice

description

Brand Voice

Extract a reusable voice profile from the founder's actual writing — so every piece of content sounds like them, not like a language model approximating them.

Required Integrations

This skill uses Unabyss MCP — agentic_query to surface real writing samples across all connected sources.

If Unabyss is not connected, tell the user: "This skill requires Unabyss MCP. You can connect it from the Tools menu."

How to Run

Step 1 — Check for an existing export

Call export_list first. If a brand voice or voice profile export already exists and is fresh, call export_read — no need to regenerate.

Step 2 — Pull writing samples with `agentic_query`

Samples are scattered across emails, posts, docs, Slack — use agentic_query:

Find examples of my own writing from across all connected sources — emails I've sent, LinkedIn or X posts, Slack messages where I'm explaining something, docs or notes I've written, launch announcements, product updates, or any other text where I'm clearly the author. Prioritize recent material. Return the actual text, not summaries.