ASO & App Marketing Skills
AI agent skills for App Store Optimization and app marketing, providing expert-level guidance for Cursor, Claude Code, and any Agent Skills-compatible assistant.
Evidence: The repository contains no code or scripts, only skill documentation and configuration. Therefore, there are no permission requests, data flow descriptions, sensitive data handling, dependency security, external effects, rollback mechanisms, or source attribution. Deduction: These aspects are entirely absent and cannot be assessed.
Evidence: Skill names and descriptions are consistent in the README, and cross-references between skills are logical, indicating good self-consistency. However, the dependency on the Appeeky API and external services is not guaranteed, and there is no documentation of failure handling or error messages. Deduction: Dependency availability is only partially mentioned, and failure messages are completely missing.
Evidence: The README details the target audience (indie developers, marketers, growth teams) and numerous use cases (keyword research, metadata optimization, etc.), capability boundaries are defined through the skill list and descriptions, trigger methods (natural language requests and /aso-skill routing) are explained, and environment fit (Cursor, Claude Code, etc.) has installation guides. Deduction: Capability boundaries do not explicitly state input/output limitations for each skill, trigger precision relies on natural language understanding without a precise trigger word list.
Evidence: Information architecture is clear, skills are organized by category, installation notes are detailed, naming is consistent (skill names correspond to directory names), examples and FAQ are provided (via example questions), license is MIT, and maintenance responsibility is indicated through CONTRIBUTING.md and PR invitations. Deduction: Known limitations are not explicitly listed, versioning and changelog are missing, and maintenance responsibility is only implied through contribution guidelines, without explicit maintainers or update policy.
Evidence: Output templates and scoring rubrics are mentioned in the README, skills are designed to provide actionable recommendations, marginal value lies in packaging expert knowledge into skills, and cost-benefit involves a paid API (Appeeky) but basic skills can be used standalone. Deduction: Output usability lacks concrete output examples, marginal value is not compared with existing tools, and cost-benefit is not analyzed in detail.
Evidence: Claims in the README (e.g., 'real App Store data') point to the Appeeky API, but no specific data sources or verification methods are provided, and facts and inferences are not clearly separated. Deduction: Claims lack traceability, there is no independent source verification, and facts and inferences are mixed.
- The repository contains only skill documentation, with no code or scripts, so actual execution behavior cannot be verified.
- Relies on external Appeeky API without availability guarantees or error handling mechanisms.
- No versioning or changelog provided, and maintenance responsibility is unclear.
What does this agent do, and when should you use it?
This repository is a collection of AI agent skills focused on App Store Optimization (ASO) and mobile app marketing, designed for indie developers, app marketers, and growth teams. It includes over 30 modular skills such as aso-audit, keyword-research, metadata-optimization, and competitor-analysis, each with battle-tested frameworks, scoring rubrics, and output templates. Skills operate by reading SKILL.md files that define the methodology, and optionally integrate with the Appeeky API to fetch real-time App Store data such as keyword rankings, competitor metadata, and download estimates. Users can invoke skills via natural language requests, and the agent will produce structured recommendations like ASO scorecards and prioritized fix lists. Installation is documented for Cursor (via remote rule or manual copy) and Claude Code (via npx skills add), and the project is MIT licensed.
The skill pack enables AI agents to perform ASO and app marketing tasks by providing pre-built instructions and frameworks. Concretely, the agent reads the SKILL.md file from the relevant skill (e.g., aso-audit, keyword-research) to understand the evaluation criteria and output format. It can then optionally call the Appeeky MCP server (https://mcp.appeeky.com/mcp) with a Bearer token to retrieve live marketplace data, including keyword volume, competitor metadata, and download estimates. For example, when asked to 'Run an ASO audit for Headspace', the agent will score the app's listing across 10 factors (e.g., title, subtitle, keywords) using the rubric, and return a scorecard with quick wins and high-impact changes. Additionally, the asc-metrics skill leverages Appeeky Connect to sync a user's App Store Connect data (downloads, revenue, subscriptions) for trend analysis. The flow is: user request → skill selection (or auto-routing via aso-router) → data fetch (if configured) → structured output.
- An indie developer wants to optimize their App Store listing before launch; they use the aso-audit skill to get a 0-100 score and a prioritized action list.
- A mobile marketer needs to find high-value keywords for a meditation app; they use keyword-research to generate a list grouped by primary, secondary, and long-tail keywords.
- A growth team wants to monitor competitors; they set up competitor-tracking to get weekly reports on metadata changes, keyword ranking shifts, and rating trends.
- A product manager plans a Christmas In-App Event; they use in-app-events to get copy suggestions, image briefs, and a submission timeline.
- An app owner notices a decline in downloads; they use market-movers to identify trending apps and understand market dynamics.
- A developer wants to reduce churn; they use retention-optimization to get activation frameworks and push notification strategies.
What are this agent's strengths and limitations?
- Comprehensive coverage: 30+ skills spanning ASO, creative assets, growth, monetization, analytics, and market intelligence.
- Actionable insights: combines static frameworks with optional live data from the Appeeky API for personalized recommendations.
- Multi-platform support: works with Cursor, Claude Code, and any agent following the Agent Skills standard.
- First-party data integration: asc-metrics skill can sync your App Store Connect data via Appeeky Connect for exact trends.
- Real-time features require an Appeeky subscription (e.g., Indie plan at $8/month) for full functionality.
- Skills are static knowledge packs that need periodic updates from the repository to stay current with ASO best practices.
- Installation involves manual steps or CLI commands, which may be a barrier for non-technical marketers.
- Documentation does not specify compatibility with ChatGPT or other major AI platforms, so adaptation may be needed.
How do you install or deploy this agent?
Installation depends on your agent environment. For Cursor: go to Settings → Rules → Add Rule → Remote Rule, and paste https://github.com/eronred/aso-skills. Alternatively, clone the repo and copy the skills folder to .cursor/skills/ (global: ~/.cursor/skills/). For Claude Code: run npx skills add eronred/aso-skills in the terminal to add all skills, or use --skill aso-audit to add specific ones. For other agents, you can manually copy the skills directory to the appropriate location (e.g., .agents/skills/). Additionally, to enable live data, configure the MCP server in your agent's settings with the URL and your API key from Appeeky.
How do you use this agent?
After installation, you can invoke skills using natural language, for example: 'Run an ASO audit for my app (id: 1617391485)' or 'Optimize my App Store title and subtitle'. You can also use the /aso-skill router to automatically dispatch your request to the right specialist. For real-time data, ensure the Appeeky MCP is configured and mention the app ID when appropriate. Direct skill invocation is also possible, e.g., /keyword-research or /competitor-analysis.
How does this agent compare with similar options?
This skill pack differentiates from traditional ASO tools like App Annie or Sensor Tower by providing AI-agent-driven guidance rather than just data analytics. Compared to generic AI assistants, it offers prebuilt ASO-specific frameworks and rubrics, leading to more standardized and expert-level outputs.