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.
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.
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 ASAscreenshot-optimization— Build CPPs for keyword-specific creativesab-test-store-listing— Test product page CVR before scaling spend
How to use apple-search-ads on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches apple-search-ads from GitHub repository eronred/aso-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★58 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.
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