Apple Search Ads logo

Apple Search Ads

CommunityPopular
Eronred
apple-search-ads

When the user wants to set up, optimize, or scale Apple Search Ads (ASA) campaigns — including keyword bidding, match types, campaign structure, Creative Product Sets, CPP routing, and ROAS optimization. Use when the user mentions "Apple Search Ads", "ASA", "Search Ads", "Search tab ads", "Today tab ads", "CPT", "TTR", "Search Match", "exact match", "broad match", "CPP in ads", "ASA bidding", or "Search Ads budget". For Meta/Google UAC/TikTok paid UA, see ua-campaign.

Overview

PublisherEronred
Repositoryaso-skills
Skill nameapple-search-ads
Stars
1.9K
Forks
116
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Eronred on GitHub. Read the source before you install it.

Installation

Install the Apple Search Ads AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Eronred/aso-skills.git /tmp/aso-skills
mkdir -p .claude/skills
cp -r /tmp/aso-skills/skills/apple-search-ads .claude/skills/apple-search-ads
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Apple Search Ads in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Apple Search Ads on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Apple Search Ads is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

PlacementWhere it appearsBest for
Search ResultsBelow the first organic result for a keywordKeyword-specific intent capture
Search TabTop of the Search tab before user typesBrand awareness, broad reach
Today TabApp Store home pageHigh-visibility brand moments
Product PagesCompetitor and related app pagesCompetitive 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 TypeHow it worksUse for
ExactOnly triggers on exact keywordHigh-value, proven terms
BroadTriggers on variations, related termsDiscovery
Search MatchApple auto-matches your app to relevant searchesDiscovery campaign only

Workflow: Use Search Match + broad in discovery. Mine the search terms report weekly. Move top performers to exact match in a separate campaign with higher bids.

Keyword Strategy

Seed List by Campaign

Brand campaign:

  • Your app name (exact)
  • Common misspellings
  • Your developer name

Competitor campaign:

  • Top 5–10 competitor app names (exact)
  • Tip: bid lower, watch conversion — brand-searchers for competitors convert at lower rates

Category campaign:

  • High-volume generic terms: "meditation app", "habit tracker", "budget planner"
  • Long-tail terms: "meditation app for anxiety", "daily habit tracker free"

Use Appeeky to validate volume and difficulty:

bash
GET /v1/keywords/metrics?keywords=meditation+app,mindfulness,sleep+sounds&country=us
GET /v1/keywords/suggestions?term=meditation&country=us

Negative Keywords

Essential to prevent waste. Add negatives at account level:

  • Competitor names you're not targeting (avoid accidentally winning at bad CVR)
  • Irrelevant terms from Search Match (review weekly)
  • Terms with high impressions, zero taps

Bidding Strategy

Starting Bids

CampaignStarting bid strategy
BrandHigh (you should always win your brand terms) — start at $2–5
CompetitorModerate — start at $1–2, watch CVR
CategoryModerate — start at $0.80–1.50
DiscoveryLow — start at $0.50–0.80

Bid Optimization Signals

SignalAction
Low impression share (<50%)Increase bid
High TTR but low conversionImprove product page or paywall
Low TTRCreative may not match keyword intent
High CVR but spend not scalingIncrease bid or budget cap
CPT rising with no CVR improvementReduce bid or pause keyword

Target CPT = Target CPI × Historical CVR (installs/taps)

Automated Bidding

ASA offers automated bidding toward a target CPA or target ROAS. Use only after:

  • Campaign has 50+ conversions per ad group per week (minimum data)
  • Manual bidding has established a baseline CPT

Creative Product Sets (CPS) and CPP Routing

Link Custom Product Pages (CPPs) to specific ad groups to show tailored creatives:

Ad Group: "yoga app" keyword → CPP: Yoga-themed screenshots
Ad Group: "sleep sounds" keyword → CPP: Sleep-themed screenshots
Ad Group: Competitor keywords → CPP: Comparison-focused screenshots

Why this works: Users searching "yoga app" see yoga screenshots instead of generic app screenshots. TTR and CVR both improve (typically +15–30%).

Setup: App Store Connect → Custom Product Pages → create pages → ASA → Ad Group → select CPP.

Metrics and Benchmarks

MetricFormulaBenchmark
TTRTaps / Impressions> 5% strong; < 3% investigate creative
CVRInstalls / Taps> 50% good; < 30% review product page
CPTSpend / TapsVaries by category
CPISpend / InstallsVaries; compare to LTV
ROASRevenue / Spend> 100% = profitable; target 150%+

Weekly Optimization Checklist

- [ ] Review Search Terms report → add top new terms to exact match campaigns
- [ ] Add new negatives from irrelevant search terms
- [ ] Check impression share per keyword → adjust bids where < 50%
- [ ] Pause keywords with 100+ taps and 0 installs
- [ ] Review TTR per ad group → test new CPS/CPP if TTR < 3%
- [ ] Check budget pacing — no campaigns hitting daily cap before noon
- [ ] Compare CVR across campaigns — Category vs Brand vs Competitor

Scaling Checklist

Before increasing budget:

- [ ] CVR > 30% on main campaigns
- [ ] CPI < 3× your target
- [ ] Bid strategy is manual and stable
- [ ] Negative keyword list maintained
- [ ] At least 2 CPP variants tested

Output Format

Campaign Audit

Account: [App Name]

Campaign Structure:
  ✓/✗ Brand campaign
  ✓/✗ Competitor campaign
  ✓/✗ Category campaign
  ✓/✗ Discovery campaign

Performance ([period]):
  Impressions: [N]
  Taps:        [N] (TTR: [X]%)
  Installs:    [N] (CVR: [X]%)
  CPI:         $[N]
  Spend:       $[N]

Top issues:
1. [issue] — [recommended fix]
2. [issue] — [recommended fix]

Priority actions:
1. [specific change] — Expected impact: [rationale]
2. [specific change] — Expected impact: [rationale]

Related Skills

  • ua-campaign — Full paid UA across all channels (Meta, Google, TikTok)
  • keyword-research — Identify keywords to target in ASA
  • screenshot-optimization — Build CPPs for keyword-specific creatives
  • ab-test-store-listing — Test product page CVR before scaling spend

Frequently asked questions

What does the Apple Search Ads AI skill do?

When the user wants to set up, optimize, or scale Apple Search Ads (ASA) campaigns — including keyword bidding, match types, campaign structure, Creative Product Sets, CPP routing, and ROAS optimization. Use when the user mentions "Apple Search Ads", "ASA", "Search Ads", "Search tab ads", "Today tab ads", "CPT", "TTR", "Search Match", "exact match", "broad match", "CPP in ads", "ASA bidding", or "Search Ads budget". For Meta/Google UAC/TikTok paid UA, see ua-campaign.

Why use Apple Search Ads on TypingMind?

Because you install it once and use it with any model. Apple Search Ads is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Apple Search Ads in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Eronred/aso-skills/tree/main/skills/apple-search-ads. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Apple Search Ads?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Apple Search Ads?

As many as you like. As long as a model supports skills, you can use Apple Search Ads with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Apple Search Ads AI skill free?

Yes. It is published on GitHub by Eronred under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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