app-store-optimization

app-store-optimization is a skill for Claude Code from frank-luongt/faos-skills-marketplace. It costs 0 tokens per session (2,993 once invoked), scanned A, original, Apache-2.0.

A guide for improving how a mobile app appears on the Apple App Store and Google Play. It covers search terms, screenshots, ratings, reviews, and store-page testing.

In plain words
What is it for?
Use it when launching an app, reviewing its store listing, researching search terms, planning screenshots or preview videos, managing reviews, or comparing Apple and Google results.
Why use it?
It helps address low visibility or too few downloads by improving the app listing for each store’s rules and audience.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the faos-analyst plugin — 22 skills, 6 commands shipped together

Good fit Use it when launching an app, reviewing its store listing, researching search terms, planning screenshots or preview videos, managing reviews, or comparing Apple and Google results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/frank-luongt/faos-skills-marketplace/app-store-optimization
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add frank-luongt/faos-skills-marketplace --skill app-store-optimization
Clone the repo
git clone --depth 1 https://github.com/frank-luongt/faos-skills-marketplace

Made for: Claude Code.

Or install faos-analyst, the plugin that ships this one along with the rest of its 22 skills, 6 commands.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for app-store-optimization

README.md
[![agentmods](https://agentmods.dev/badge/skills/frank-luongt/faos-skills-marketplace/app-store-optimization/github.svg)](https://agentmods.dev/skills/frank-luongt/faos-skills-marketplace/app-store-optimization)
Your own site
<a href="https://agentmods.dev/skills/frank-luongt/faos-skills-marketplace/app-store-optimization"><img src="https://agentmods.dev/badge/skills/frank-luongt/faos-skills-marketplace/app-store-optimization/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for app-store-optimization

Your own site · 80×15
<a href="https://agentmods.dev/skills/frank-luongt/faos-skills-marketplace/app-store-optimization"><img src="https://agentmods.dev/badge/skills/frank-luongt/faos-skills-marketplace/app-store-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,993 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.02993
Opus 5 $0.00000 $0.01496
Sonnet 5 $0.00000 $0.00599
Haiku 4.5 $0.00000 $0.00299

Measured 12d ago against content hash dd113b7e0bc1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

app-store-optimization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/faos-analyst/skills/app-store-optimization/SKILL.md · 316 lines

How it starts

The opening of the file, as written. The whole thing — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.


name: app-store-optimization description: Optimize mobile app listings for iOS App Store and Google Play — keyword research, screenshot design, rating management, and conversion optimization. Use when launching a mobile app, improving app store rankings, or optimizing download conversion rates. tags: [aso, mobile, app-store, growth]

App Store Optimization (ASO)

Comprehensive ASO framework for iOS App Store and Google Play — keyword research, listing optimization, visual asset strategy, rating management, and A/B testing store pages. The mobile equivalent of SEO.

Use this skill when

  • Launching a new mobile app on iOS or Google Play
  • Optimizing an existing app listing to improve rankings
  • Conducting keyword research for app store visibility
  • Designing or testing screenshots and app preview videos
  • Managing app ratings and reviews strategically
  • Comparing performance across Apple and Google platforms
  • Preparing ASO audit reports

Do not use this skill when

  • Optimizing a website for Google search (use seo-audit)
  • Running paid user acquisition campaigns (use campaign-analytics)
  • Designing the app's UX/UI itself (use UX/frontend skills)
  • Building the app's technical architecture (use engineering skills)

Instructions

  1. Research keywords using the ASO Keyword Research Framework.
  2. Optimize listing elements — title, subtitle, description, category.
  3. Design visual assets — screenshots, preview video, icon.
  4. Set up rating management — review response templates and prompt strategy.
  5. Run store listing experiments — A/B test on Google Play, Product Page Optimization on iOS.
  6. Produce the ASO Audit Report with keyword priority matrix.

Platform Differences: Apple vs. Google

Understanding these differences is critical — ASO strategy must be platform-specific:

Factor Apple App Store Google Play
Title 30 characters 30 characters
Subtitle 30 characters N/A (use short description)
Short Description N/A 80 characters
Long Description Not indexed for search Indexed for search — keywords matter here
Keyword Field 100 characters (hidden, comma-separated) N/A — keywords from title + description
Category Primary + secondary Primary + secondary + tags
Screenshots Up to 10 per device Up to 8 per device
Preview Video Up to 3 (30 sec, auto-play muted) 1 (auto-play on listing)
A/B Testing Product Page Optimization (3 treatments) Store Listing Experiments (5 variants)
Rating Reset Can reset on major version Cannot reset — cumulative
Review Response Developer can respond Developer can respond

Read the full file on GitHub · 316 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 316 lines · 0 tokens per session scan A dd113b7e0bc1

Subscribe to this mod's changes

app-store-optimization is a skill published in the GitHub repository frank-luongt/faos-skills-marketplace (33 stars, last pushed 2mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,993 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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