agentmods.dev

app-store-optimization

app-store-optimization is a skill for Claude Code, Codex from cass-2003/local-workflow-skill. It costs 103 tokens per session (3,761 once invoked), scanned A, original, MIT.

A guide to improving an app’s listing and search visibility in the Apple App Store and Google Play Store. ASO means App Store Optimization.

In plain words
What is it for?
Finding and ranking keywords, reviewing competitor listings, and drafting or improving app titles, subtitles, short descriptions, and full store descriptions.
Why use it?
It helps replace guesswork with keyword research, competitor checks, and deliberate placement of words in titles and descriptions.

Skill for Claude CodeCodex

not rated 12 1mo ago A scan 103 tokens original MIT
View the source file cass-2003/local-workflow-skill
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.

agentmods
npx agentmods add skills/cass-2003/local-workflow-skill/app-store-optimization
Any agent
npx skills add cass-2003/local-workflow-skill --skill app-store-optimization
Clone the repo
git clone --depth 1 https://github.com/cass-2003/local-workflow-skill

Made for: Claude Code, 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/cass-2003/local-workflow-skill/app-store-optimization.svg)](https://agentmods.dev/skills/cass-2003/local-workflow-skill/app-store-optimization)
Your own site
<a href="https://agentmods.dev/skills/cass-2003/local-workflow-skill/app-store-optimization"><img src="https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/app-store-optimization.svg" alt="Measured on agentmods" 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 3,761 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00103 $0.03761
Opus 5 $0.00051 $0.01880
Sonnet 5 $0.00021 $0.00752
Haiku 4.5 $0.00010 $0.00376

Measured 4d ago against content hash 125f2393441d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 4d 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

Copies of this mod

1 near-identical copy found in the catalogue:

skills/app-store-growth/community/app-store-optimization/SKILL.md · 475 lines

How it starts

The opening of the file, as written. The whole thing — 475 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 · 475 lines

Files

What ships with it

16 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 4d ago First seen · 475 lines · 103 tokens per session scan A 125f2393441d

Subscribe to this mod's changes

app-store-optimization is a skill published in the GitHub repository cass-2003/local-workflow-skill (12 stars, last pushed 1mo ago), licensed MIT. It adds 103 tokens to every session and 3,761 once invoked, about $0.0005 per session on Opus 5. 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.

Research
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