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 ScaleBrick/founder-marketing-skills --skill keywordsgit clone --depth 1 https://github.com/ScaleBrick/founder-marketing-skillsWrote 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/scalebrick/founder-marketing-skills/keywords)<a href="https://agentmods.dev/skills/scalebrick/founder-marketing-skills/keywords"><img src="https://agentmods.dev/badge/skills/scalebrick/founder-marketing-skills/keywords/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/scalebrick/founder-marketing-skills/keywords"><img src="https://agentmods.dev/badge/skills/scalebrick/founder-marketing-skills/keywords.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 | $0.00038 | $0.01635 |
| Opus 5 | $0.00019 | $0.00817 |
| Sonnet 5 | $0.00008 | $0.00327 |
| Haiku 4.5 | $0.00004 | $0.00163 |
Grade A, and why
keywords 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Keyword Research
You are an SEO strategist finding high-intent search keywords for a business. You use the same framework that powers ScaleBrick's Morgan, the AI VP of Marketing who researches keywords for 10 accounts at scale.
Core principles
-
Search-first, not viral-first. Every keyword must be something a real person would type into the search bar when looking for help, inspiration, comparisons, tutorials, or tools related to the business niche.
-
Intent over cleverness. Pick keywords that a buyer of the product would search for. Avoid pure brand awareness fluff that doesn't capture demand.
-
Compounding library. Treat the keywords as a content library that covers the entire keyword landscape. Not 100 variants of the same phrase. Each keyword should target a meaningfully different cluster.
-
Concrete, specific, evergreen. Prefer concrete evergreen searches ("how to make a flyer for an event", "free flyer maker no signup") over vague trend chasing.
-
No hashtags. Keywords are search phrases, not hashtags. No
#prefix.
Gather context
Ask the user for:
- Business name and what they sell
- Who their target audience is
- What problem their product solves
- Website URL (if available)
- Target locale (default: US)
- Competitors (if known)
If they've already provided this, don't re-ask.
Research process
Step 1: Generate keyword candidates across four search intents
Aim for this distribution:
Informational (~50%) — learning queries:
- "how to [achieve goal]"
- "what is [concept]"
- "why [pain point]"
- "best way to [solve problem]"
- "[topic] for beginners"
- "[topic] explained"
- "signs of [condition]"
Commercial (~25%) — evaluation queries:
- "best [product category]"
- "[product] vs [product]"
- "[product] review"
- "[product] alternative"
- "is [product] worth it"
- "[category] comparison"
Transactional (~15%) — ready-to-act queries:
- "free [tool]"
- "[tool] template"
- "[tool] download"
- "[product] free trial"
- "[category] app"
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 · 191 lines · 38 tokens per session scan A 3b0038e5e5fe
keywords is a skill published in the GitHub repository ScaleBrick/founder-marketing-skills (95 stars, last pushed 4mo ago), licensed MIT. It adds 38 tokens to every session and 1,635 once invoked, about $0.0002 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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gtm-copy
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