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/brainbytes-dev/everything-claude-marketingWrote 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/brainbytes-dev/everything-claude-marketing/brand-review)<a href="https://agentmods.dev/commands/brainbytes-dev/everything-claude-marketing/brand-review"><img src="https://agentmods.dev/badge/commands/brainbytes-dev/everything-claude-marketing/brand-review/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/brainbytes-dev/everything-claude-marketing/brand-review"><img src="https://agentmods.dev/badge/commands/brainbytes-dev/everything-claude-marketing/brand-review.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 | $0.00023 | $0.01479 |
| Opus 5.5 | $0.00009 | $0.00592 |
| Sonnet 5.5 | $0.00005 | $0.00296 |
| Haiku 4.5 | $0.00002 | $0.00148 |
Grade A, and why
brand-review 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 2mo 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/brand-review
Review any content for brand consistency, covering voice, tone, messaging alignment, terminology, and visual guidelines compliance. Receive a scored assessment with specific fixes.
What This Command Does
The /brand-review command acts as your brand quality gate. It evaluates content against your brand standards and returns a structured assessment with an overall consistency score, specific findings organized by severity, and concrete revision recommendations. It catches the subtle inconsistencies that erode brand trust — off-tone language, unapproved terminology, messaging that contradicts your positioning, and guideline violations.
The command delegates to the brand-guardian agent, which maintains awareness of brand voice attributes, messaging frameworks, and content standards to perform systematic evaluations.
When to Use
- You are publishing content and want a brand consistency check before it goes live
- You have new team members or external agencies producing content that needs review
- You want to audit existing content (website pages, emails, sales decks) for brand alignment
- You are rolling out updated brand guidelines and need to assess current content
- You are reviewing translated or localized content for brand consistency
- Multiple teams produce content and you want to ensure a unified voice
- You want to establish a baseline brand consistency score for your content library
How It Works
- Content Ingestion — Reads and analyzes the provided content in full
- Voice Assessment — Evaluates whether the content matches your brand voice attributes (e.g., professional but approachable, confident but not arrogant)
- Tone Calibration — Checks if the tone is appropriate for the content type, audience, and context
- Messaging Alignment — Verifies that claims, value propositions, and positioning statements align with approved messaging frameworks
- Terminology Audit — Flags unapproved terms, inconsistent product names, or competitor language patterns
- Style & Format Check — Reviews against style guidelines (capitalization, punctuation, formatting conventions)
- Scoring & Recommendations — Produces an overall score with prioritized, specific revision suggestions
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.
- 2mo ago First seen · 155 lines · 23 tokens per session scan A e0cda9c3ee92
brand-review is a command published in the GitHub repository brainbytes-dev/everything-claude-marketing (10 stars, last pushed 7mo ago), licensed MIT. It adds 23 tokens to every session and 1,479 once invoked, about $0.0001 per session on Opus 5.5. 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.
brand-review compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| brand-reviewbrainbytes-dev/everything-claude-marketing | Command | 10 | 7mo ago | MIT | A | 23 | Claude Code |
| checklistgithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| clarifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 27 | Claude Code |
| specifygithub/spec-kit | Command | 148k | yesterday | MIT | A | 13 | Claude Code |
| analyzegithub/spec-kit | Command | 148k | yesterday | MIT | A | 26 | Claude Code |
| convergegithub/spec-kit | Command | 148k | yesterday | MIT | A | 35 | Claude Code |
| implementgithub/spec-kit | Command | 148k | yesterday | MIT | A | 14 | Claude Code |