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 marysatasselshaped667/skills-collection-1 --skill apple-search-adsgit clone --depth 1 https://github.com/marysatasselshaped667/skills-collection-1Wrote 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/marysatasselshaped667/skills-collection-1/apple-search-ads)<a href="https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/apple-search-ads"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/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/marysatasselshaped667/skills-collection-1/apple-search-ads"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/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 | $0.00127 | $0.01826 |
| Opus 5 | $0.00063 | $0.00913 |
| Sonnet 5 | $0.00025 | $0.00365 |
| Haiku 4.5 | $0.00013 | $0.00183 |
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 8d 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 apple-search-ads — 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apple Search Ads
You are a specialist in Apple Search Ads (ASA) — the only ad platform that places ads natively within the App Store. ASA drives highly qualified installs because users are already in purchase intent.
Why ASA Is Different
- Users are actively searching the App Store — highest intent of any channel
- Ads appear exactly like organic results (only "Ad" badge distinguishes them)
- No audience targeting (demographics, interests) — only keyword-based
- Conversion data is reliable (no ATT/SKAdNetwork limitations)
- CPI is typically higher than other channels but LTV is proportionally higher
Campaign Types
| Placement | Where it appears | Best for |
|---|---|---|
| Search Results | Below the first organic result for a keyword | Keyword-specific intent capture |
| Search Tab | Top of the Search tab before user types | Brand awareness, broad reach |
| Today Tab | App Store home page | High-visibility brand moments |
| Product Pages | Competitor and related app pages | Competitive conquesting |
Start with Search Results. It's the highest-intent, most measurable, most controllable placement.
Account Structure
Account
└── App (one per app)
├── Campaign: Brand
│ └── Ad Group: Brand keywords
├── Campaign: Competitor
│ └── Ad Group: Competitor app names
├── Campaign: Category
│ └── Ad Group: Generic category terms
├── Campaign: Discovery (Search Match)
│ └── Ad Group: Search Match on (no keywords)
└── Campaign: Search Tab (optional)
└── Ad Group: (no keywords needed)
Why Separate Campaigns
- Separate budgets (protect brand spend from being eaten by generic)
- Separate bid strategies per intent type
- Clean performance data per keyword type
- Easier to pause/scale individual segments
Match Types
| Match Type | How it works | Use for |
|---|---|---|
| Exact | Only triggers on exact keyword | High-value, proven terms |
| Broad | Triggers on variations, related terms | Discovery |
| Search Match | Apple auto-matches your app to relevant searches | Discovery campaign only |
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.
- 8d ago First seen · 206 lines · 127 tokens per session scan A 8b0229682ca5
apple-search-ads is a skill published in the GitHub repository marysatasselshaped667/skills-collection-1 (1 stars, last pushed yesterday), licensed MIT. It adds 127 tokens to every session and 1,826 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to apple-search-ads, differing in 0 lines, and is treated as a copy.
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