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 dalroot/agentic-seo --skill landing-pagegit clone --depth 1 https://github.com/dalroot/agentic-seoWrote 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/dalroot/agentic-seo/landing-page)<a href="https://agentmods.dev/skills/dalroot/agentic-seo/landing-page"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/landing-page/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/dalroot/agentic-seo/landing-page"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/landing-page.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 | $0.00093 | $0.01995 |
| Opus 5.5 | $0.00037 | $0.00798 |
| Sonnet 5.5 | $0.00019 | $0.00399 |
| Haiku 4.5 | $0.00009 | $0.00199 |
Grade A, and why
landing-page-generator 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pages: Landing Page
Guides campaign landing page structure, conversion flow, and optimization. Primary use: Paid ads (PPC/SEM) — landing pages are typically built to receive paid traffic; ad-to-page alignment is critical for conversion. See paid-ads-strategy for when to use paid ads and ad-to-page alignment principles. Also applies to affiliate signup, product launch, lead capture, webinar registration, and other single-goal conversion pages. Scale: When building many landing pages (city-specific, product-specific, integration-specific), use programmatic-seo (template + data) and template-page-generator for template design. Differs from homepage (multi-purpose) and product pages (catalog).
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Paid Ads: LP Requirements
When LP receives paid traffic: ad promise on page immediately; mobile-first (CTA above fold, fast load); minimal form (fewer fields); trust above fold. See paid-ads-strategy for full ad-to-page alignment and Quality Score.
Initial Assessment
Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read it for product, audience, and value proposition.
Identify:
- Page goal: Signup, purchase, lead capture, webinar, download
- Traffic source: Paid ads, email, affiliate, organic
- Audience: Cold vs warm; segment if known
Landing Page Structure (5-Step Flow)
| Step | Purpose | Elements |
|---|---|---|
| 1. Stop the scroll | Capture attention in ~2.6 seconds | Headline, subheadline, hero image or video |
| 2. Earn trust | Social proof before the ask | Logos, testimonials, ratings, customer count |
| 3. Explain value | Benefits, features, use cases | Clear copy; who it's for, what it does |
| 4. Remove doubt | Objection handling | FAQ, guarantees, comparison |
| 5. Make the ask | Single primary CTA | One clear action; repeat at logical points |
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 · 137 lines · 93 tokens per session scan A 8526ef9a75fc
landing-page-generator is a skill published in the GitHub repository dalroot/agentic-seo (25 stars, last pushed 6d ago), licensed MIT. It adds 93 tokens to every session and 1,995 once invoked, about $0.0004 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 2026-10-26.
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