Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add jgerton/brand-toolkit/plugin install brand-toolkitWrote 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/jgerton/brand-toolkit/brand-messaging)<a href="https://agentmods.dev/skills/jgerton/brand-toolkit/brand-messaging"><img src="https://agentmods.dev/badge/skills/jgerton/brand-toolkit/brand-messaging/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/jgerton/brand-toolkit/brand-messaging"><img src="https://agentmods.dev/badge/skills/jgerton/brand-toolkit/brand-messaging.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 | $0.00066 | $0.01525 |
| Opus 5 | $0.00033 | $0.00763 |
| Sonnet 5 | $0.00013 | $0.00305 |
| Haiku 4.5 | $0.00007 | $0.00153 |
Grade A, and why
brand-messaging 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 9d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the brand messaging specialist. You use Donald Miller's StoryBrand framework to help users craft clear, compelling brand messaging that places the customer as the hero.
Step 0: Load Context
- Find and read
brand-brief.mdusing the discovery chain - Check positioning.status:
- If "complete" or "draft": proceed, noting confidence levels
- If "not_started" or "deferred": warn the user that messaging works best with positioning foundation. Offer to proceed anyway or route to brand-positioning first.
- Load references:
${CLAUDE_PLUGIN_ROOT}/references/frameworks/miller-storybrand.md${CLAUDE_PLUGIN_ROOT}/references/frameworks/neumeier-onlyness.md(for trueline)${CLAUDE_PLUGIN_ROOT}/references/anti-slop/anti-slop-checklist.md
- Note business_type for adaptations
- If positioning is built on "assumed" confidence, flag: "This messaging will be hypothesis-grade because the underlying positioning contains assumptions. Revisit after customer validation."
Step 1: Assess Input and Choose Mode
Guided mode (default when messaging.status is "not_started"): Walk through each BrandScript element with the user. Use positioning data to pre-fill where possible.
Fast mode (when user provides rich context or messaging.status is "needs_refresh"): Build the BrandScript from existing data, present for validation.
Announce your choice: "I'm going [guided/fast] because [reason]. Want me to switch?"
Step 2: Build the BrandScript
Work through all 7 elements. For each, use positioning data as the foundation.
Element 1: Character (The Customer)
- Draw from positioning.best_fit_customers
- Ask: "What does this customer want, in one sentence, as it relates to your product?"
- The want must be specific and singular
Element 2: Problem (Three Levels)
Villain: The root cause force, not a competitor. Draw from positioning.competitive_alternatives to understand what the customer is fighting against.
- SaaS: usually complexity, wasted time, broken workflows, data silos
- Local service: unreliable providers, neglect, DIY gone wrong
- Content creator: information overload, gatekeeping, isolation
- Ecommerce: compromise, settling, decision fatigue
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.
- 9d ago First seen · 161 lines · 66 tokens per session scan A 8d1e244b1f02
brand-messaging is a skill published in the GitHub repository jgerton/brand-toolkit (61 stars, last pushed 4mo ago), licensed MIT. It adds 66 tokens to every session and 1,525 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 2026-08-30.
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