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 itsjwill/ad-audit-pro --skill ads-landinggit clone --depth 1 https://github.com/itsjwill/ad-audit-proWrote 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/itsjwill/ad-audit-pro/ads-landing)<a href="https://agentmods.dev/skills/itsjwill/ad-audit-pro/ads-landing"><img src="https://agentmods.dev/badge/skills/itsjwill/ad-audit-pro/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/itsjwill/ad-audit-pro/ads-landing"><img src="https://agentmods.dev/badge/skills/itsjwill/ad-audit-pro/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 | $0.00063 | $0.01410 |
| Opus 5 | $0.00032 | $0.00705 |
| Sonnet 5 | $0.00013 | $0.00282 |
| Haiku 4.5 | $0.00006 | $0.00141 |
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 10d 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 ads-landing — 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Landing Page Quality for Ad Campaigns
Process
- Collect landing page URLs from active ad campaigns
- Read
ads/references/benchmarks.mdfor conversion rate benchmarks - Read
ads/references/conversion-tracking.mdfor pixel/tag verification - Assess each landing page for ad-specific quality factors
- Score landing pages and identify improvement opportunities
- Generate recommendations prioritized by conversion impact
Message Match Assessment
The #1 landing page issue in ad campaigns — does the page match the ad?
What to Check
- Headline match: landing page H1 reflects ad copy headline/keyword
- Offer match: promoted offer (price, discount, trial) is visible above fold
- CTA match: landing page CTA matches ad's promised action
- Visual match: consistent imagery between ad creative and page
- Keyword match: search keyword appears naturally in page content
Message Match Scoring
| Level | Description | Score |
|---|---|---|
| Exact match | Headline, offer, CTA all align perfectly | 100% |
| Partial match | Headline matches but offer/CTA differs | 60% |
| Weak match | Generic page, loosely related to ad | 30% |
| Mismatch | Page content doesn't reflect ad promise | 0% |
Page Speed Assessment
Slow pages kill conversion rates. For every 1s delay, CVR drops ~7%.
Thresholds (Ad Landing Pages)
| Metric | Pass | Warning | Fail |
|---|---|---|---|
| LCP | <2.5s | 2.5-4.0s | >4.0s |
| FID/INP | <100ms | 100-200ms | >200ms |
| CLS | <0.1 | 0.1-0.25 | >0.25 |
| Time to Interactive | <3.0s | 3.0-5.0s | >5.0s |
| Page weight | <2MB | 2-5MB | >5MB |
Common Speed Issues in Ad Pages
- Hero images not compressed (use WebP/AVIF)
- Too many third-party scripts (chat widgets, analytics, heatmaps)
- Render-blocking CSS/JS above fold
- No lazy loading for below-fold content
- Font files not preloaded
Mobile Experience
75%+ of ad clicks come from mobile. Mobile experience is critical.
Mobile Checklist
- Tap targets: ≥48x48px with ≥8px spacing
- Font size: ≥16px body text (no pinch-to-zoom needed)
- Form fields: properly sized, keyboard type matches input (email, phone, number)
- CTA button: full-width on mobile, visible without scrolling
- No horizontal scroll
- Images responsive and properly sized
- Phone number clickable (tel: link)
- No interstitials or popups blocking content on load
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.
- 10d ago First seen · 160 lines · 63 tokens per session scan A c77bd9ed85d2
ads-landing is a skill published in the GitHub repository itsjwill/ad-audit-pro (9 stars, last pushed 6mo ago), licensed MIT. It adds 63 tokens to every session and 1,410 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ads-landing, differing in 0 lines, and is treated as a copy.
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