# ASO Skills: App Store Optimization Knowledge Packaged for AI Agents

> Eronred/aso-skills is a repository of MDX skill files that give AI coding agents like Cursor and Claude Code expert-level App Store Optimization guidance. Each skill file encodes a specific ASO workflow, from keyword research and metadata writing to competitor analysis and seasonal campaigns, and pulls live App Store data through the Appeeky API.

**Eronred/aso-skills** — AI agent skills for App Store Optimization (ASO) and app marketing. Built for indie developers, app marketers, and growth teams who want Cursor, Claude Code, or any Agent Skills-compatible AI assistant to help with keyword research, metadata optimization, competitor analysis, and app growth.

- Repository: https://github.com/Eronred/aso-skills
- Website: https://appeeky.com/
- Stars: 2,113 · Forks: 126
- Language: MDX
- License: MIT
- Published: 2026-09-10 · Updated: 2026-09-10 · Language: en
- Canonical page: https://hysenlabs.com/projects/eronred-aso-skills

## What ASO Skills is and who it targets

App Store Optimization knowledge is scattered across blog posts, paid courses, and consultant engagements. Eronred/aso-skills takes a different distribution approach: it encodes ASO frameworks, scoring rubrics, and output templates into MDX files that an AI agent can read and execute.

The result is that asking a Cursor or Claude Code session to run an ASO audit or find keywords for an app produces structured output from a skill file, not a generic LLM response drawn from training data alone. The README describes the core proposition: the agent reads the skill, pulls real data from the App Store via Appeeky, and gives actionable recommendations.

The target users are indie developers who publish apps and manage their own store listings, app marketers who are responsible for keyword rankings and metadata, and growth teams who want to automate parts of competitive analysis and seasonal campaign planning.

## Installing the skills in Cursor or Claude Code

There are three installation paths documented in the README. For Cursor:

```
npx skills add eronred/aso-skills -a cursor
```

For Claude Code:

```
npx skills add eronred/aso-skills
```

For a manual installation that copies the skill files directly:

```
git clone https://github.com/eronred/aso-skills.git && cp -r aso-skills/skills/* .cursor/skills/
```

After installation, the skills are invoked by asking the agent naturally or by using the slash commands directly. The README includes examples:

```
"Run an ASO audit for my app (id: 1617391485)"
"Find the best keywords for a meditation app"
"Optimize my App Store title and subtitle"
"What apps are rising in the charts right now?"
```

The /aso-router skill handles routing automatically if you do not want to remember the individual skill names. It accepts a natural language request and directs it to the relevant specialist skill.

## The skills catalog: 30-plus workflows across four categories

The README organizes the skills into four groups.

ASO Core covers the fundamental workflows: /aso-audit scores a listing across ten factors on a 0-100 scale and produces a prioritized fix list; /keyword-research finds keywords by volume times difficulty times relevance and groups them into primary, secondary, and long-tail buckets; /metadata-optimization writes title, subtitle, keyword field, and description with three variants and character counts; /competitor-analysis delivers keyword gaps, a creative teardown, and specific opportunities; /seasonal-aso produces a keyword calendar with a metadata swap strategy; /android-aso handles Google Play-specific optimization including indexed description strategy and rating recovery.

Creative and International covers screenshot optimization (ten-slot strategy), app preview video scripting, icon design principles with A/B testing guidance, Custom Product Pages for Apple, review management with the HEAR response framework, and localization with a per-country keyword research approach.

Growth covers an eight-week launch timeline, Apple Search Ads campaign structure, creator and UGC marketing programs, referral mechanics with K-factor math, web-to-app install funnels, App Store featuring readiness, In-App Events planning, App Clips setup, and press outreach.

Revenue and Retention covers monetization strategy, paywall optimization, and subscription lifecycle management.

## How the Appeeky API connection works

The skills pull live App Store data through the Appeeky API, documented at docs.appeeky.com. This is what distinguishes aso-skills from a static prompt library: keyword volume, difficulty scores, chart positions, and competitor data are real numbers from the App Store rather than illustrative placeholders.

The README also mentions the Appeeky Local App Growth desktop application, a native macOS app that syncs App Store Connect, Google Play, Apple Search Ads, Meta/TikTok ads, and RevenueCat into a local cache. The desktop app includes a built-in terminal that pairs with MCP, allowing the same agent skills to operate against locally cached data. This means the skills can work offline against the cached dataset after an initial sync, rather than requiring a live API call for every query.

The Appeeky API dependency means any team that adopts these skills is also tied to Appeeky's availability and pricing model. The README does not document rate limits, authentication requirements, or cost for the Appeeky API, so those details require consulting docs.appeeky.com separately before production use.

## Repository structure: MDX files and how they are organized

The primary language of the repository is MDX. The top-level directory includes a skills/ folder that contains the individual skill files referenced in the skills table. Each skill is a directory entry under skills/.

Additional top-level files include introduction.mdx, quickstart.mdx, and index.mdx, which are the documentation entry points. The how-skills-work.mdx file explains the skill execution model. The CLAUDE.md and AGENTS.md files at the top level provide agent-specific instructions that AI coding agents read automatically on startup.

The reference/ and guides/ directories contain supplementary documentation. The docs.json file is likely a configuration for the documentation site (the Mintlify .mintignore file at the root confirms this). The validate-skills.sh script provides a way to validate skill file syntax.

The ua-skills repository at github.com/appeeky/ua-skills is a companion collection that covers user acquisition channels: TikTok ads, Meta ads, Apple Search Ads, ad creatives, and ROAS. It is separate from aso-skills and targets the paid acquisition side of growth rather than organic store optimization.

## Limitations: what ASO Skills does not provide

The skills are frameworks and templates. The quality of the output depends on the AI agent's ability to execute the skill file instructions, the quality of the Appeeky API data for the app category and region in question, and how precisely the user's prompt triggers the right skill.

The /aso-audit skill scores listings across ten factors, but the scoring methodology is internal to the skill file and is not independently validated. A high audit score under this system does not guarantee ranking improvements.

The repository has no GitHub releases. There is no versioned artifact. Skills are updated in place on the main branch, so a skill invoked today may produce different output than one invoked after a future update without the user being notified.

The skills do not replace direct access to App Store Connect analytics. They work from the outside view of an app's public listing and from the Appeeky API data, not from first-party install or revenue data unless the Appeeky desktop app has synced App Store Connect credentials.

## Comparison with using LLMs directly for ASO

A developer can ask any LLM assistant to help write App Store metadata or suggest keywords without installing any skill files. The difference is that an unstructured prompt produces generic LLM output based on training data that may be months or years old. ASO Skills adds two things: a structured workflow (the skill file encodes the sequence of analysis steps) and live App Store data via the Appeeky API.

Other paid ASO tools like AppFollow, Sensor Tower, and AppFigures provide live App Store data with their own dashboards and export features. None of them operate inside an IDE or produce output that flows directly into a developer's code session. ASO Skills trades the richer UI and deeper data catalog of those tools for tighter integration with the AI coding agent environment.

## Maintenance and license

The repository is not archived. The last push was on 2026-08-22. There are no GitHub releases. The MIT license applies to the skill files and documentation in the repository. The Appeeky API and the Appeeky desktop app are external products with their own terms of service, not covered by the repository's MIT license.

## Conclusion

Eronred/aso-skills is useful for indie developers and app marketers who already use Cursor or Claude Code in their workflow and want to bring ASO analysis into that environment rather than switching to a separate ASO tool. It is a poor fit for teams that need programmatic access to raw keyword data without an AI agent layer, or for anyone not comfortable with the Appeeky API dependency for live App Store data. Before adding the skills, confirm that your agent setup supports the npx skills add command or the manual file copy approach, and verify that the Appeeky API key is configured if you want the skills to pull real search volume and difficulty data rather than relying on the skill frameworks alone.

## FAQ

### Does ASO Skills require an Appeeky API key to work?

The skills pull live App Store data through the Appeeky API, which means an API key is required for features like keyword volume, difficulty scores, and competitor data. The README does not document the authentication setup, so you need to consult docs.appeeky.com for API access details.

### Can ASO Skills be used with AI agents other than Cursor and Claude Code?

The README states the skills are built for any Agent Skills-compatible AI assistant, not only Cursor and Claude Code. The repository name references the Agent Skills standard at agentskills.io, and the npx skills add command targets that ecosystem.

### What is the difference between ASO Skills and the ua-skills repository?

ASO Skills covers organic App Store Optimization: keyword research, metadata writing, screenshot strategy, competitor analysis, and review management. The companion ua-skills repository at github.com/appeeky/ua-skills covers paid user acquisition channels: TikTok ads, Meta ads, Apple Search Ads, and ad creatives.

## Sources

- [Eronred/aso-skills on GitHub](https://github.com/Eronred/aso-skills)
- [Issues](https://github.com/Eronred/aso-skills/issues)
- [License: MIT](https://github.com/Eronred/aso-skills/blob/main/LICENSE)
- [Project website](https://appeeky.com/)
- [README](https://github.com/Eronred/aso-skills/blob/main/README.md)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/eronred-aso-skills