App Store Optimization
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
Install
$ agentstack add skill-cbrock84-headcount-app-store-optimization ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-cbrock84-headcount-app-store-optimization) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
App store optimization
Two levers, and they are separate problems: being found, and being installed once found. Diagnose which is failing before changing anything.
Being found
The stores index different fields, so the same metadata does not work on both.
- App name / title — the single heaviest field. Brand plus the primary descriptive term. Do not
spend it on brand alone.
- Subtitle and keyword field — no repetition across fields; duplicated terms are wasted
characters, not reinforcement.
- Long description — indexed on one store, effectively not on the other. Write it for the store
that indexes it and for humans on the store that does not.
- Category — pick where you can rank, not where you technically belong.
Target terms with real intent. Ranking first for a term nobody searches is a vanity result.
Being installed
Most visitors decide from the first screenshot and the rating, without scrolling or reading.
- Screenshots — the first two carry the decision. Lead with the outcome or the core screen, with
a caption stating the benefit. Never lead with an onboarding or login screen.
- Icon — recognizable at actual size, distinct from category conventions. Test at real scale on
a device.
- Rating — the strongest single conversion factor. Prompt for review after a success moment,
never on launch or mid-task.
- Video — only if it demonstrates something a screenshot cannot. A weak one costs installs.
Reviews
Respond to negative reviews specifically and without defensiveness, naming the fix and its version where there is one. Prospects read the responses as much as the complaints, and a pattern of real answers converts.
Watch review text for recurring themes — it is the cheapest continuous product research available.
Testing
Change one element at a time and let it run a full weekly cycle; app traffic is strongly day-of-week seasonal. Attributing a lift to the wrong change is worse than not testing.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: cbrock84
- Source: cbrock84/headcount
- License: MIT
- Homepage: https://cbrock84.github.io/headcount/
Install and usage instructions live in the source repository linked above.
Reviews
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Versions
- v0.1.0 Imported from the upstream source.