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.
npx skills add rediumvex/ai-marketing-claude --skill market-brandgit clone --depth 1 https://github.com/rediumvex/ai-marketing-claudeWrote 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/rediumvex/ai-marketing-claude/market-brand)<a href="https://agentmods.dev/skills/rediumvex/ai-marketing-claude/market-brand"><img src="https://agentmods.dev/badge/skills/rediumvex/ai-marketing-claude/market-brand/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/rediumvex/ai-marketing-claude/market-brand"><img src="https://agentmods.dev/badge/skills/rediumvex/ai-marketing-claude/market-brand.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 | $0.00086 | $0.01400 |
| Opus 5 | $0.00043 | $0.00700 |
| Sonnet 5 | $0.00017 | $0.00280 |
| Haiku 4.5 | $0.00009 | $0.00140 |
Grade A, and why
market-brand 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 13d 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Market Brand — Voice, Tone, and Archetype
Analyze how a brand communicates across every touchpoint and produce a usable voice guide that a new copywriter could pick up and write on-brand within an hour.
Source material (in priority order)
- Homepage — the most curated representation
- About page — how they describe themselves
- Product / pricing pages — how they sell
- Blog (3–5 recent posts) — extended voice
- Social profiles — conversational voice
- Error / empty states / microcopy — unguarded voice
- Email or newsletter if accessible
- Job postings — internal culture signal
Use scripts/analyze_page.py to pull raw text from each page.
The 4 Voice Dimensions (score each 1–10)
Formal ↔ Casual
Signals: contractions, sentence length, "we/you" vs "the company/one", greetings, slang.
Serious ↔ Playful
Signals: exclamation marks, emoji, puns, metaphors, error message style, self-deprecation.
Technical ↔ Simple
Signals: jargon density, acronym explanation, detail depth, assumed expertise.
Reserved ↔ Bold
Signals: hedged claims ("may help") vs direct claims ("guaranteed"), opinionated stances, competitor mentions.
Every score needs 3 quoted examples from the source material — no score without evidence.
Voice map (ASCII visualization)
Formal |----------[●]-----------| Casual (6/10)
Serious |-------[●]--------------| Playful (4/10)
Technical |-----------------[●]----| Simple (7/10)
Reserved |--------------[●]-------| Bold (6/10)
Archetype Detection (Jung's 12, collapsed to 12 for brand use)
Map the brand to 1 primary and optionally 1 secondary archetype:
| Archetype | Driver | Voice cue |
|---|---|---|
| Sage | Wisdom, truth | Educational, cited, measured |
| Innocent | Simplicity, optimism | Warm, direct, positive |
| Explorer | Freedom, discovery | Adventurous, independent |
| Hero | Mastery, courage | Bold, challenge-oriented |
| Outlaw | Disruption | Provocative, anti-establishment |
| Magician | Transformation | Visionary, "imagine if" |
| Regular Guy/Gal | Belonging | Plain-spoken, relatable |
| Lover | Intimacy, beauty | Sensual, emotional, curated |
| Jester | Joy, play | Witty, irreverent, memeable |
| Caregiver | Service, protection | Nurturing, supportive |
| Creator | Craft, imagination | Artisan, detail-loving |
| Ruler | Control, quality | Authoritative, premium |
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.
- 13d ago First seen · 173 lines · 86 tokens per session scan A 5d61c9cf702c
market-brand is a skill published in the GitHub repository rediumvex/ai-marketing-claude (38 stars, last pushed 5mo ago), licensed MIT. It adds 86 tokens to every session and 1,400 once invoked, about $0.0004 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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