app-store-optimization

app-store-optimization is a skill for Codex from bestagentkits/agency-skills. It costs 103 tokens per session (3,830 once invoked), scanned A, a copy of app-store-optimization, MIT.

A toolkit for improving how an app appears in the Apple App Store and Google Play Store. App Store Optimization means choosing search terms and listing text that can help people find an app.

In plain words
What is it for?
Use it to research keywords, compare competitor rankings, plan titles and descriptions, and map search terms to app-store listing fields.
Why use it?
It helps replace guesswork about app-store wording with keyword research, competitor analysis, and organized metadata suggestions.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to research keywords, compare competitor rankings, plan titles and descriptions, and map search terms to app-store listing fields.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/bestagentkits/agency-skills/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 bestagentkits/agency-skills --skill app-store-optimization
Clone the repo
git clone --depth 1 https://github.com/bestagentkits/agency-skills

Made for: Codex.

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/bestagentkits/agency-skills/app-store-optimization/github.svg)](https://agentmods.dev/skills/bestagentkits/agency-skills/app-store-optimization)
Your own site
<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/app-store-optimization"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/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/bestagentkits/agency-skills/app-store-optimization"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/app-store-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 103 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,830 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 97% copy Near-identical to another mod 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 $0.00103 $0.03830
Opus 5 $0.00051 $0.01915
Sonnet 5 $0.00021 $0.00766
Haiku 4.5 $0.00010 $0.00383

Measured 10d ago against content hash 82b3229826b3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, 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 10d ago.

The scan reads SKILL.md. This mod also ships 8 executable files (scripts/ab_test_planner.py, scripts/aso_scorer.py, scripts/competitor_analyzer.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

This is a copy

97% identical to app-store-optimization — 16 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/claude-skills/app-store-optimization/SKILL.md · 487 lines

How it starts

The opening of the file, as written. The whole thing — 487 lines — stays where its author put it; the contents beside it link to each section on GitHub.

App Store Optimization (ASO)


Keyword Research Workflow

Discover and evaluate keywords that drive app store visibility.

Workflow: Conduct Keyword Research

  1. Define target audience and core app functions:
    • Primary use case (what problem does the app solve)
    • Target user demographics
    • Competitive category
  2. Generate seed keywords from:
    • App features and benefits
    • User language (not developer terminology)
    • App store autocomplete suggestions
  3. Expand keyword list using:
    • Modifiers (free, best, simple)
    • Actions (create, track, organize)
    • Audiences (for students, for teams, for business)
  4. Evaluate each keyword:
    • Search volume (estimated monthly searches)
    • Competition (number and quality of ranking apps)
    • Relevance (alignment with app function)
  5. Score and prioritize keywords:
    • Primary: Title and keyword field (iOS)
    • Secondary: Subtitle and short description
    • Tertiary: Full description only
  6. Map keywords to metadata locations
  7. Document keyword strategy for tracking
  8. Validation: Keywords scored; placement mapped; no competitor brand names included; no plurals in iOS keyword field

Keyword Evaluation Criteria

Factor Weight High Score Indicators
Relevance 35% Describes core app function
Volume 25% 10,000+ monthly searches
Competition 25% Top 10 apps have <4.5 avg rating
Conversion 15% Transactional intent ("best X app")

Keyword Placement Priority

Location Search Weight
App Title Highest
Subtitle (iOS) High
Keyword Field (iOS) High
Short Description (Android) High
Full Description Medium

See: references/keyword-research-guide.md


Metadata Optimization Workflow

Optimize app store listing elements for search ranking and conversion.

Workflow: Optimize App Metadata

  1. Audit current metadata against platform limits:
    • Title character count and keyword presence
    • Subtitle/short description usage
    • Keyword field efficiency (iOS)
    • Description keyword density
  2. Optimize title following formula:
    [Brand Name] - [Primary Keyword] [Secondary Keyword]
    
  3. Write subtitle (iOS) or short description (Android):
    • Focus on primary benefit
    • Include secondary keyword
    • Use action verbs
  4. Optimize keyword field (iOS only):
    • Remove duplicates from title
    • Remove plurals (Apple indexes both forms)
    • No spaces after commas
    • Prioritize by score
  5. Rewrite full description:
    • Hook paragraph with value proposition
    • Feature bullets with keywords
    • Social proof section
    • Call to action
  6. Validate character counts for each field
  7. Calculate keyword density (target 2-3% primary)
  8. Validation: All fields within character limits; primary keyword in title; no keyword stuffing (>5%); natural language preserved

Read the full file on GitHub · 487 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. 10d ago First seen · 487 lines · 103 tokens per session scan A 82b3229826b3

Subscribe to this mod's changes

app-store-optimization is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 103 tokens to every session and 3,830 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to app-store-optimization, differing in 16 lines, and is treated as a copy.

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