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 pedrol-cmd/brain-drin --skill drin-brand-voicegit clone --depth 1 https://github.com/pedrol-cmd/brain-drinWrote 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/pedrol-cmd/brain-drin/drin-brand-voice)<a href="https://agentmods.dev/skills/pedrol-cmd/brain-drin/drin-brand-voice"><img src="https://agentmods.dev/badge/skills/pedrol-cmd/brain-drin/drin-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/pedrol-cmd/brain-drin/drin-brand-voice"><img src="https://agentmods.dev/badge/skills/pedrol-cmd/brain-drin/drin-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 | $0.00048 | $0.00711 |
| Opus 5 | $0.00024 | $0.00356 |
| Sonnet 5 | $0.00010 | $0.00142 |
| Haiku 4.5 | $0.00005 | $0.00071 |
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 7d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Voice Profile Builder
Extract and codify a consistent voice from source material so all content sounds like the same person.
When to Use
- Setting up content strategy for a new channel
- Ensuring consistency across LinkedIn, email, website
- Onboarding a new writer or AI tool to match existing voice
- Refining voice after analyzing what performs best
Process
Step 1: Gather Source Material
Ask the user for 5-10 examples of writing they like (their own or aspirational):
- LinkedIn posts that performed well
- Emails that got replies
- Messages that felt natural
- Writing from others they want to emulate
Step 2: Extract Patterns
Analyze the source material for:
Sentence Structure
- Average sentence length (short/medium/long)
- Rhythm pattern (short-short-long? Varied? Punchy?)
- Paragraph length
- Use of fragments vs complete sentences
Tone Markers
- Formality level (1-5 scale)
- Humor frequency and type
- Directness level
- Emotion display (reserved vs expressive)
Vocabulary
- Words they use often (signature phrases)
- Words they never use (anti-patterns)
- Jargon level (technical vs accessible)
- Filler words to avoid
Structural Habits
- How they open (question? statement? story?)
- How they close (CTA? question? insight?)
- Use of lists, headers, bold
- Capitalization patterns
Step 3: Build Voice Profile
# Voice Profile: [Name/Brand]
## Core Voice
[2-3 sentences describing the overall voice character]
## Do
- [Pattern to follow — with example from source material]
- [Pattern to follow — with example]
- [Pattern to follow — with example]
## Don't
- [Anti-pattern — with example of what to avoid]
- [Anti-pattern — with example]
- [Anti-pattern — with example]
## Sentence Rhythm
[Description of typical sentence patterns]
## Signature Phrases
- [Phrase they use naturally]
- [Phrase they use naturally]
## Banned Words
- [Word/phrase to never use]
- [Word/phrase to never use]
## Tone by Channel
| Channel | Formality | Length | Special Notes |
|---------|-----------|--------|---------------|
| LinkedIn | [1-5] | [range] | [notes] |
| Email | [1-5] | [range] | [notes] |
| WhatsApp | [1-5] | [range] | [notes] |
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.
- 7d ago First seen · 107 lines · 48 tokens per session scan A 64aefc34f671
brand-voice is a skill published in the GitHub repository pedrol-cmd/brain-drin (11 stars, last pushed 5mo ago), licensed MIT. It adds 48 tokens to every session and 711 once invoked, about $0.0002 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 2026-09-03.
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