apple-search-ads

eronred/aso-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/eronred/aso-skills --skill apple-search-ads
0 commentsdiscussion
summary

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.

skill.md

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

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:

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

Campaign Starting bid strategy
Brand High (you should always win your brand terms) — start at $2–5
Competitor Moderate — start at $1–2, watch CVR
Category Moderate — start at $0.80–1.50
Discovery Low — start at $0.50–0.80

Bid Optimization Signals

Signal Action
Low impression share (<50%) Increase bid
High TTR but low conversion Improve product page or paywall
Low TTR Creative may not match keyword intent
High CVR but spend not scaling Increase bid or budget cap
CPT rising with no CVR improvement Reduce 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

Metric Formula Benchmark
TTR Taps / Impressions > 5% strong; < 3% investigate creative
CVR Installs / Taps > 50% good; < 30% review product page
CPT Spend / Taps Varies by category
CPI Spend / Installs Varies; compare to LTV
ROAS Revenue / 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
how to use apple-search-ads

How to use apple-search-ads on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add apple-search-ads
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/eronred/aso-skills --skill apple-search-ads

The skills CLI fetches apple-search-ads from GitHub repository eronred/aso-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/apple-search-ads

Reload or restart Cursor to activate apple-search-ads. Access the skill through slash commands (e.g., /apple-search-ads) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.858 reviews
  • Ganesh Mohane· Dec 16, 2024

    We added apple-search-ads from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ishan Abbas· Dec 12, 2024

    Solid pick for teams standardizing on skills: apple-search-ads is focused, and the summary matches what you get after install.

  • Omar Liu· Dec 8, 2024

    apple-search-ads is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Soo White· Nov 27, 2024

    Keeps context tight: apple-search-ads is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Rahul Santra· Nov 7, 2024

    apple-search-ads reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Hana Khan· Nov 3, 2024

    apple-search-ads has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Pratham Ware· Oct 26, 2024

    apple-search-ads is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Liam Ghosh· Oct 22, 2024

    apple-search-ads fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Soo Kim· Oct 18, 2024

    We added apple-search-ads from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Harper Jain· Sep 25, 2024

    Solid pick for teams standardizing on skills: apple-search-ads is focused, and the summary matches what you get after install.

showing 1-10 of 58

1 / 6