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.
git clone --depth 1 https://github.com/stefanoskarakasis/Product-Marketing-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/commands/stefanoskarakasis/product-marketing-skills/brand-voice)<a href="https://agentmods.dev/commands/stefanoskarakasis/product-marketing-skills/brand-voice"><img src="https://agentmods.dev/badge/commands/stefanoskarakasis/product-marketing-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/commands/stefanoskarakasis/product-marketing-skills/brand-voice"><img src="https://agentmods.dev/badge/commands/stefanoskarakasis/product-marketing-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 | $0.00026 | $0.00305 |
| Opus 5 | $0.00013 | $0.00152 |
| Sonnet 5 | $0.00005 | $0.00061 |
| Haiku 4.5 | $0.00003 | $0.00030 |
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 8d 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.
What it actually says
/pmm-positioning:brand-voice -- Brand Voice
Build a voice guide that survives contact with a blank page — brand personality with edges, tone mapped per buying-committee persona, a channel table, and a forbidden-language list, every instruction backed by an example. Deepens brain Section 4 in place, on confirmation.
Invocation
/pmm-positioning:brand-voice Build our voice guide
/pmm-positioning:brand-voice Our copy feels off — audit it
/pmm-positioning:brand-voice Write this LinkedIn post in our voice for the Champion persona
Workflow
Uses the brand-voice skill. Loads brain Section 4 (current guide,
however thin) and Section 2 (ICP/personas) if present — pulling from a
recent buyer-personas session instead of guessing the committee when
one exists — establishes personality and its edges, maps tone per
persona, builds the channel table and forbidden-language list, then
deepens brain Section 4 with the result — showing the exact before/after
first, writing only on confirmation. Closes with a session log to
/context/skill-sessions.md.
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.
- 8d ago First seen · 31 lines · 26 tokens per session scan A 540459e56b45
brand-voice is a command published in the GitHub repository stefanoskarakasis/Product-Marketing-Skills (5 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 305 once invoked, about $0.0001 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 2026-09-04.
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