App Store Optimization
ASO toolkit for keyword research, metadata optimization, and tracking.
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See how the App Store Optimization skill responds to actual requests. These are representative examples of what your AI agent will produce.
Our app is a time tracking tool for freelancers. Current App Store ranking: we're not ranking for 'time tracker' (too competitive), but we get some installs from 'freelance time tracking.' How do we build a better keyword strategy?
App Store Keyword Strategy: Freelance Time Tracker
Current state: Ranking for "freelance time tracking" is a win — that's a high-intent, lower-competition keyword. Build from there.
Keyword research methodology:
- Use AppFollow or AppTweak (or Sensor Tower) to pull keyword rankings and volume
- Analyze top 3 competitors' keyword lists
- Identify your "keyword clusters" — groups of related terms
Keyword clusters to target:
| Cluster | Keywords | Competition | Priority |
|---|---|---|---|
| Core job | freelance time tracker, time log app, work hours tracker | Medium | High |
| Billing | invoice timer, billable hours tracker, client billing app | Low-Medium | High |
| Audience | freelancer productivity, consultant time tracking | Low | Medium |
| Integration | Toggl alternative, Harvest time tracking app | Low | Medium |
Metadata optimization:
Current title (example): "TimeLog — Time Tracker" Optimized title: "TimeLog: Freelance Time Tracker" — puts highest-value keyword in title (highest weight field)
Subtitle (30 chars): "Bill clients, track hours" — uses billing cluster
Keyword field (100 chars, iOS only — no spaces, comma-separated):
billable,hours,invoice,consultant,timer,log,client,work,productivity,freelancer
Rules: No spaces in keyword field, no repeated words from title/subtitle, use singular OR plural (not both), no competitor names.
Expected impact: Title keyword change typically improves ranking for that keyword by 15–40 positions within 2–4 weeks of the next App Store index cycle.
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Skills are designed for AI coding agents (Claude Code, Cursor, Windsurf) and IDE-based workflows where the agent can read files, run scripts, and act on your codebase. Web-based AI can use the knowledge and frameworks, but won't have tool access.
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