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 markdavidgan/apple-dev-skills --skill apple-search-adsgit clone --depth 1 https://github.com/markdavidgan/apple-dev-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/markdavidgan/apple-dev-skills/apple-search-ads)<a href="https://agentmods.dev/skills/markdavidgan/apple-dev-skills/apple-search-ads"><img src="https://agentmods.dev/badge/skills/markdavidgan/apple-dev-skills/apple-search-ads/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/markdavidgan/apple-dev-skills/apple-search-ads"><img src="https://agentmods.dev/badge/skills/markdavidgan/apple-dev-skills/apple-search-ads.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 | $0.00130 | $0.02739 |
| Opus 5 | $0.00065 | $0.01370 |
| Sonnet 5 | $0.00026 | $0.00548 |
| Haiku 4.5 | $0.00013 | $0.00274 |
Grade A, and why
apple-search-ads 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.
How it starts
The opening of the file, as written. The whole thing — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apple Search Ads
Run the highest-intent paid acquisition channel available to iOS apps — directly inside the App Store, where users are already searching to download.
Apple Search Ads is structurally unlike every other UA channel. Users are not scrolling social feeds or watching videos; they are standing in the App Store with fingers ready to tap Install. That intent advantage means ASA delivers installs that behave more like organic users than paid users on any other network.
Why ASA Is Different
| Property | ASA | Every other paid channel |
|---|---|---|
| User intent | Actively searching to download | Interrupted from another activity |
| Targeting axis | Keyword only | Audience (demo, interest, behavioral) |
| ATT / SKAN gap | None — Apple owns the conversion signal | Significant signal loss post-iOS 14 |
| Ad appearance | Indistinguishable from organic results | Clearly an ad unit |
| Conversion data | First-party, deterministic | Modeled or probabilistic |
The no-ATT gap is the most underrated advantage. Every CPI, CVR, and ROAS figure you read out of ASA is real — not a model estimate. That makes optimization decisions reliable in a way that Meta/Google campaigns rarely are post-iOS 14.
Campaign Placements
| Placement | Where it appears | Best use |
|---|---|---|
| Search Results | Immediately below the first organic result when a user searches | Primary driver — highest intent, most controllable |
| Search Tab | Top of the Search tab before any query is typed | Broad awareness; reach users before they form intent |
| Today Tab | App Store home page | Brand moments, major launches |
| Product Pages | Below the listing of a competitor or related app | Competitive conquesting |
Start with Search Results only. It is the most measurable and most efficient placement at every stage of scale. Layer in other placements only after Search Results is profitable and stable.
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 · 264 lines · 130 tokens per session scan A 4862eeed21e9
apple-search-ads is a skill published in the GitHub repository markdavidgan/apple-dev-skills (5 stars, last pushed 11d ago), licensed MIT. It adds 130 tokens to every session and 2,739 once invoked, about $0.0006 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 2026-08-31.
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