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 tal7aouy/marketkit --skill market-brandgit clone --depth 1 https://github.com/tal7aouy/marketkitWrote 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/tal7aouy/marketkit/market-brand)<a href="https://agentmods.dev/skills/tal7aouy/marketkit/market-brand"><img src="https://agentmods.dev/badge/skills/tal7aouy/marketkit/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/tal7aouy/marketkit/market-brand"><img src="https://agentmods.dev/badge/skills/tal7aouy/marketkit/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 | $0.00000 | $0.03884 |
| Opus 5.5 | $0.00000 | $0.01554 |
| Sonnet 5.5 | $0.00000 | $0.00777 |
| Haiku 4.5 | $0.00000 | $0.00388 |
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 1mo 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.
This is a copy
100% identical to market-brand — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 472 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Voice Analysis and Guidelines Generation
Skill Purpose
Analyze a brand's voice, tone, and messaging across all available channels and generate a comprehensive brand voice guidelines document. This skill examines how a brand communicates, identifies patterns and inconsistencies, and produces actionable guidelines that any writer or marketer can follow to maintain brand consistency.
When to Use
- User wants to understand or document a brand's voice
- User needs brand voice guidelines for a team, freelancers, or agency
- User wants to ensure consistency across marketing channels
- User is rebranding or refining their brand identity
- User wants to compare their brand voice to competitors
- Triggered by
/market brand <url>or/market brand
How to Execute
Step 1: Gather Source Material
To analyze a brand's voice, examine content from multiple sources. Prioritize in this order:
Primary Sources (must analyze):
- Homepage -- The most curated representation of the brand
- About page -- How the brand describes itself
- Product/service pages -- How they present their offerings
Secondary Sources (analyze if available): 4. Blog posts (at least 3-5 recent posts) 5. Social media profiles (bio, recent posts, engagement style) 6. Email newsletters (welcome email, recent sends) 7. Customer-facing copy (error messages, onboarding flows, help docs)
Tertiary Sources: 8. Job postings -- Reveals internal culture and values 9. Press releases -- Formal communication style 10. Ad copy -- Paid messaging approach 11. Video scripts or podcast transcripts -- Spoken brand voice
Use browser tools or the analyze_page.py script to access web content. For social media, check the website for social links and analyze the linked profiles.
Step 2: Voice Dimension Analysis
Map the brand's voice along four primary dimensions. Each dimension is a spectrum, not a binary.
Dimension 1: Formal <-----> Casual
Where does the brand fall on the formality spectrum?
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.
- 1mo ago First seen · 472 lines · 0 tokens per session scan A d4dc4754d593
market-brand is a skill published in the GitHub repository tal7aouy/marketkit (2 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,884 tokens. A static security scan graded it A with 0 findings. It is 100% identical to market-brand, differing in 0 lines, and is treated as a copy.
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