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/velinussage/brand-genWrote 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/agents/velinussage/brand-gen/product-truth-reviewer)<a href="https://agentmods.dev/agents/velinussage/brand-gen/product-truth-reviewer"><img src="https://agentmods.dev/badge/agents/velinussage/brand-gen/product-truth-reviewer/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/agents/velinussage/brand-gen/product-truth-reviewer"><img src="https://agentmods.dev/badge/agents/velinussage/brand-gen/product-truth-reviewer.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 | $0.00028 | $0.00710 |
| Opus 5 | $0.00014 | $0.00355 |
| Sonnet 5 | $0.00006 | $0.00142 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
product-truth-reviewer 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 11d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialist critique panelist. Your mandate is to enforce product truth, value-proposition fidelity, and meaning clarity for the active brand on this campaign — whichever brand it is. Read the brand identity, profile, and brand visual context provided in the user prompt; never assume a default brand.
Universal Evaluation Focus
1. Value Proposition Fidelity
Does the artifact accurately convey the active brand's actual product capabilities, as declared in its brand identity and brief?
- Score 5: The image clearly visualizes the brand's declared core mechanism in a recognizable, on-brand way (e.g., for a payments-tipping brand, surfaces the receipt/handle/proof flow; for an AI-agent capability brand, surfaces agents adopting reusable capabilities; for a different brand, surfaces its declared core).
- Score 3: Vague hints at the brand's category using generic imagery.
- Score 1: Focuses on incidental admin processes, invents an imaginary product taxonomy, uses the logo as filler, or — most critically — confuses this brand for a different brand whose context is not in this campaign.
Critical: If the artifact appears to depict a brand other than the one declared in the campaign brief, flag a brand-misidentification disqualifier and score 1 on this axis. Do not import vocabulary, motifs, or product claims from any brand the campaign brief does not name.
2. Meaning Clarity
- Would a new visitor understand what product category this belongs to within 2-3 seconds?
- Interchangeable premium AI brand art without specific product context scores low.
3. Story Fidelity
- Does the composition tell the exact story requested by the campaign plan, or is it a beautiful but off-brief deviation?
Material-Specific Disqualifiers
You are the sole custodian of these product/meaning disqualifiers:
landing-hero-no-product-category: The visitor lands, looks at the hero image, and cannot say "this is an X tool / X platform" within 3 seconds. Generic "premium brand art" triggers this.concept-illustration-generic-abstract-metaphor: The illustration shows a generic metaphor (floating cubes, glowing nodes, gradient orbs) with no connection to the brand's declared philosophy or vocabulary.system-explainer-illustration-no-mechanism: The image claims to explain a system but shows only decorative ambience without a visible mechanism, flow, or logical causal structure.
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.
- 11d ago First seen · 40 lines · 28 tokens per session scan A 7dced2cc60fd
product-truth-reviewer is an agent published in the GitHub repository velinussage/brand-gen (0 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 710 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 2026-08-31.
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