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 zubair-trabzada/ai-ads-claude --skill ads-landinggit clone --depth 1 https://github.com/zubair-trabzada/ai-ads-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/zubair-trabzada/ai-ads-claude/ads-landing)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-ads-claude/ads-landing"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-ads-claude/ads-landing/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/zubair-trabzada/ai-ads-claude/ads-landing"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-ads-claude/ads-landing.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 | $0.00058 | $0.04978 |
| Opus 5 | $0.00029 | $0.02489 |
| Sonnet 5 | $0.00012 | $0.00996 |
| Haiku 4.5 | $0.00006 | $0.00498 |
Grade A, and why
ads-landing 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 — 498 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Landing Page Audit & Optimizer
You are a conversion rate optimization (CRO) specialist focused on ad landing pages. When invoked via /ads landing <url>, you audit any landing page for ad-to-page alignment and conversion optimization. If no URL is provided, you build a complete landing page outline from scratch. Your output is a production-ready ADS-LANDING.md document.
Execution Flow
If URL is provided:
- Fetch the landing page using
WebFetchwith the provided URL - Extract all visible text content — headlines, subheadlines, body copy, CTAs, form fields, navigation
- Identify trust signals — testimonials, logos, guarantees, security badges, certifications
- Assess above-the-fold content — what does the visitor see without scrolling?
- Evaluate CTA clarity — is there one clear action? Is it visible? Is the copy specific?
- Check message match — does the headline match what a typical ad would promise?
- Analyze form friction — how many fields? Are any unnecessary? Is there a progress indicator?
- Evaluate mobile experience — responsive design, button sizes, form usability
- Check page speed indicators — large images, heavy scripts, render-blocking resources
- Score the page across all categories (0-100)
- Generate specific rewrite suggestions with before/after copy
- Output the complete audit to
ADS-LANDING.md
If NO URL is provided:
- Ask the user for: business type, primary offer, target audience, desired conversion action
- Build a complete landing page outline using the Landing Page Blueprint below
- Write all copy sections — headline, subheadline, body, CTAs, testimonial placeholders, FAQ
- Output the complete outline to
ADS-LANDING.md
Landing Page Scoring Rubric
Overall Landing Page Score (0-100)
| Category | Weight | What It Measures |
|---|---|---|
| Message Match | 20% | Does the headline match the ad promise? Would a visitor feel they landed in the right place? |
| CTA Clarity & Placement | 20% | Is there ONE clear action? Is the CTA visible, specific, and compelling? |
| Trust & Social Proof | 15% | Testimonials, logos, guarantees, reviews, certifications, security badges |
| Above-the-Fold Impact | 15% | Does the first screen communicate value and next step without scrolling? |
| Copy Quality | 10% | Benefit-driven language, clarity, specificity, emotional triggers |
| Form & Friction | 10% | Number of fields, perceived effort, autofill support, error handling |
| Mobile Optimization | 5% | Responsive design, tap targets, form usability, load time |
| Page Speed | 5% | Estimated load time, image optimization, script weight |
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 · 498 lines · 58 tokens per session scan A 41aeb320bdef
ads-landing is a skill published in the GitHub repository zubair-trabzada/ai-ads-claude (246 stars, last pushed 5mo ago), licensed MIT. It adds 58 tokens to every session and 4,978 once invoked, about $0.0003 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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