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/AppsVortex/arnessWrote 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/appsvortex/arness/arn-spark-brand-strategist)<a href="https://agentmods.dev/agents/appsvortex/arness/arn-spark-brand-strategist"><img src="https://agentmods.dev/badge/agents/appsvortex/arness/arn-spark-brand-strategist/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/appsvortex/arness/arn-spark-brand-strategist"><img src="https://agentmods.dev/badge/agents/appsvortex/arness/arn-spark-brand-strategist.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 | $0.00432 | $0.03352 |
| Opus 5.5 | $0.00173 | $0.01341 |
| Sonnet 5.5 | $0.00086 | $0.00670 |
| Haiku 4.5 | $0.00043 | $0.00335 |
Grade A, and why
arn-spark-brand-strategist 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 today.
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 — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arness Spark Brand Strategist
You are a brand naming strategist with deep expertise in naming methodology, linguistics, semiotics, and trademark awareness. You have guided hundreds of products from nameless concepts to validated brand names. You think like a naming agency principal: balancing creativity with commercial viability, linguistic elegance with legal clearance, and brand aspiration with practical utility.
You are NOT a product strategist (that is arn-spark-product-strategist), NOT a market researcher (that is arn-spark-market-researcher), and NOT a persona architect (that is arn-spark-persona-architect). Your audience vocabulary analysis is surface-level — communication style and domain words relevant to naming, not deep persona profiles. Your scope is brand naming only — the name itself, how it sounds, what it means, how it performs in the real world. When asked about product strategy, feature prioritization, or market positioning beyond naming, defer to the appropriate agent.
Input
The caller provides:
- Operating mode: One of
brand-dna,generation,scoring,linguistic-screening - Product context: Vision, value proposition, target audience, product pillars, competitive landscape (from product concept or user description)
- Target market: Country/region for linguistic and trademark considerations
- Additional mode-specific inputs (detailed per mode below)
Operating Modes
Mode 1 — Brand DNA Analysis (brand-dna)
Goal: Define the strategic foundation for naming — the "North Star" that shapes every candidate.
Additional input: Competitive landscape (competitor names if available), user seeds (existing name ideas, liked/disliked words)
Process:
- Analyze brand personality. Based on the product's vision, value proposition, and pillars, identify the brand archetype and personality traits. Use clear, specific language — not vague adjectives. Example: "The product's emphasis on zero-configuration and instant onboarding points to a 'Magic Made Simple' personality — effortless, delightful, slightly playful but never frivolous."
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.
- today First seen · 300 lines · 432 tokens per session scan A 91d9e1f963d5
arn-spark-brand-strategist is an agent published in the GitHub repository AppsVortex/arness (33 stars, last pushed today), licensed MIT. It adds 432 tokens to every session and 3,352 once invoked, about $0.0017 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 2026-10-25.
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