Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Vibe-Marketer/plugins-and-skills --skill brand-voicegit clone --depth 1 https://github.com/Vibe-Marketer/plugins-and-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vibe-marketer/plugins-and-skills/brand-voice)<a href="https://agentmods.dev/skills/vibe-marketer/plugins-and-skills/brand-voice"><img src="https://agentmods.dev/badge/skills/vibe-marketer/plugins-and-skills/brand-voice/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vibe-marketer/plugins-and-skills/brand-voice"><img src="https://agentmods.dev/badge/skills/vibe-marketer/plugins-and-skills/brand-voice.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00065 | $0.01116 |
| Opus 5 | $0.00032 | $0.00558 |
| Sonnet 5 | $0.00013 | $0.00223 |
| Haiku 4.5 | $0.00006 | $0.00112 |
Grade A, and why
brand-voice scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<quick_start> Provide any of these for voice extraction:
- Existing website copy, emails, or social posts
- Content the brand admires (competitors, influences)
- Recorded calls, podcasts, or transcripts from the founder
- Existing brand guidelines or style docs
- Examples of "this sounds like us" vs. "this doesn't"
I will generate:
- Voice DNA profile (core patterns, rhythms, vocabulary)
- Do/Don't examples for every voice dimension
- Word bank (preferred words, banned words, replacements)
- Sentence templates that capture the rhythm
- Voice checklist for reviewing any piece of copy </quick_start>
<essential_principles>
<voice_extraction_method>
- Collect samples. Minimum 5 pieces of content the brand considers "on-voice." More is better.
- Find the patterns. Sentence length distribution. Paragraph length. Question frequency. Contraction usage. Profanity comfort level. Jargon density. First-person vs. second-person ratio.
- Identify the personality blend. Every brand voice is a blend of 2-3 archetypes. Name them specifically (e.g., "Dan Kennedy directness + Gary Halbert humor + Hormozi specificity").
- Document the vocabulary. Words they always use. Words they never use. Industry terms they embrace vs. avoid.
- Capture the rhythm. Short punchy sentences mixed with longer storytelling runs? All short? Academic? Fragment-heavy? This is the hardest part and the most important.
- Create the contrast. For every rule, show what the voice sounds like AND what it doesn't sound like. Without contrast, rules are meaningless. </voice_extraction_method>
<voice_dimensions> Score each dimension on a spectrum with specific evidence:
- Formality — Academic ←→ Casual (contractions, slang, sentence fragments)
- Humor — Serious ←→ Playful (jokes, self-deprecation, wit)
- Authority — Peer ←→ Expert (credentials, directness, teaching style)
- Emotion — Reserved ←→ Expressive (exclamation marks, emphatic words, vulnerability)
- Pace — Measured ←→ Urgent (sentence length, paragraph length, CTA frequency)
- Specificity — General ←→ Granular (numbers, names, exact details)
- Profanity — Clean ←→ Raw (comfort with strong language)
- Perspective — Third-person ←→ First-person (I/we/you ratios) </voice_dimensions>
<output_format> Every brand voice guide must include:
Voice DNA
- 2-3 sentence summary of the voice ("sounds like X talking to Y about Z")
- Personality blend (named influences)
- One-line acid test ("If this copy could be from any brand, it fails")
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 105 lines · 65 tokens per session scan A ccc41c2967f9
brand-voice is a skill published in the GitHub repository Vibe-Marketer/plugins-and-skills (2 stars, last pushed 6mo ago), licensed MIT. It adds 65 tokens to every session and 1,116 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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