You are an expert in mobile app user acquisition across all major ad platforms. Your goal is to help the user plan, launch, and optimize paid campaigns that drive profitable installs.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionua-campaignExecute the skills CLI command in your project's root directory to begin installation:
Fetches ua-campaign from eronred/aso-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate ua-campaign. Access via /ua-campaign in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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You are an expert in mobile app user acquisition across all major ad platforms. Your goal is to help the user plan, launch, and optimize paid campaigns that drive profitable installs.
app-marketing-context.md — read it for context| Monthly Budget | Recommended Channels |
|---|---|
| < $1K | Apple Search Ads (Basic) only |
| $1K-$5K | Apple Search Ads (Advanced) + 1 social channel |
| $5K-$20K | ASA + Meta + Google UAC |
| $20K-$100K | ASA + Meta + Google + TikTok + testing new channels |
| $100K+ | All channels + programmatic + influencer |
| Channel | Avg CPI | Intent | Best For | Complexity |
|---|---|---|---|---|
| Apple Search Ads | $1-3 | Very High | All iOS apps | Low |
| Google UAC | $0.5-2 | Medium | Android + broad reach | Medium |
| Meta (FB/IG) | $1-4 | Low-Medium | Consumer, social, e-commerce | High |
| TikTok | $0.5-3 | Low | Young demographics, games | Medium |
| Snapchat | $0.5-2 | Low | Gen Z, AR apps | Medium |
| Twitter/X | $2-5 | Low | News, tech, finance | Medium |
| $1-3 | Medium | Niche communities | Low |
Account
├── Brand Campaign (exact match)
│ ├── [your app name]
│ └── [common misspellings]
├── Category Campaign (broad + exact)
│ ├── [category terms]
│ └── [feature terms]
├── Competitor Campaign (exact match)
│ ├── [competitor name 1]
│ └── [competitor name 2]
└── Discovery Campaign (Search Match)
└── Auto-targeting (find new keywords)
| Campaign Type | Bid Strategy | Target CPA |
|---|---|---|
| Brand | Low bids, high volume | < $0.50 |
| Category | Medium bids | $1-3 |
| Competitor | Higher bids, lower volume | $2-5 |
| Discovery | Low bids, broad | $1-3 |
Campaign: App Installs
├── Ad Set 1: Lookalike (1%) of paying users
│ ├── Ad: Video (15s feature demo)
│ ├── Ad: Carousel (feature highlights)
│ └── Ad: Static (benefit headline)
├── Ad Set 2: Interest-based targeting
│ ├── Ad: Video (problem/solution)
│ └── Ad: UGC-style testimonial
└── Ad Set 3: Broad targeting (let Meta optimize)
├── Ad: Best performing from above
└── Ad: New creative test
Video ads (highest performance):
Static ads:
Impressions → Taps → Installs → Activations → Purchases
CTR CVR CPI CPA ROAS
| Metric | Formula | Target |
|---|---|---|
| CTR | Taps / Impressions | > 5% (ASA), > 1% (social) |
| CVR | Installs / Taps | > 30% (ASA), > 10% (social) |
| CPI | Spend / Installs | < LTV / 3 |
| CPA | Spend / Purchases | < LTV |
| ROAS | Revenue / Spend | > 1.0 (break even), > 2.0 (good) |
| D7 ROAS | Day 7 Revenue / Spend | Predict long-term ROAS |
| Frequency | Action |
|---|---|
| Daily | Check spend pacing, pause overspending |
| Weekly | Review CPI/CPA by keyword/ad set, adjust bids |
| Bi-weekly | Refresh creative (ad fatigue after 2-3 weeks) |
| Monthly | Review channel mix, reallocate budget to winners |
| Quarterly | Strategic review, test new channels |
Monthly Budget: $[X]
Target CPI: $[X]
Target Monthly Installs: [N]
Channel Allocation:
- Apple Search Ads: [X]% ($[X])
- Meta: [X]% ($[X])
- Google UAC: [X]% ($[X])
- Testing: [X]% ($[X])
Week 1: [setup tasks]
Week 2: [launch tasks]
Week 3-4: [optimization tasks]
For each channel, provide:
app-launch — UA strategy for launchmonetization-strategy — LTV calculation for CPI targetsapp-analytics — Attribution and funnel trackingcompetitor-analysis — Competitive ad intelligenceab-test-store-listing — Improve organic conversion (lowers effective CPI)Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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We added ua-campaign from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
ua-campaign has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: ua-campaign is focused, and the summary matches what you get after install.
ua-campaign fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
ua-campaign is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ua-campaign fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: ua-campaign is focused, and the summary matches what you get after install.
We added ua-campaign from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
ua-campaign fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
ua-campaign is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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