aso-skills: ASO playbooks packaged as agent skills for Cursor and Claude Code

The gap aso-skills is trying to close

ASO advice is abundant and mostly unstructured. Blog posts describe what a good subtitle looks like. Courses explain keyword difficulty. Consultants charge for the same material with your app name filled in. The repository's own framing is blunt about this: most ASO knowledge lives in blog posts, courses, and expensive consultants, and the project's stated goal is to package that knowledge into skills any AI agent can use. The target reader is an indie developer or a small growth team that already has an AI assistant open in an editor and wants ASO output that is specific rather than generic. That is a narrower audience than it first appears. If you do not use Cursor, Claude Code, or another Agent Skills-compatible assistant, this repository has nothing to offer you beyond readable markdown.

What a skill file actually is here

The repository is MDX, not code. Each directory under skills/ is a skill: a set of instructions, frameworks, scoring rubrics, and output templates that the agent reads before answering. The README describes the loop plainly. The agent reads the skill, pulls real data from the App Store via Appeeky, and returns recommendations. So the mechanism is retrieval plus prompt, not a compiled program. There is no runtime, no daemon, no schema migration. The aso-audit skill scores a listing across ten factors on a 0 to 100 scale and produces a prioritized fix list. The metadata-optimization skill writes a title, subtitle, keyword field, and description with three variants and character counts. Those output shapes are the actual product. An agent with no skill file can still write you a subtitle; what it will not do consistently is apply the same ten factors in the same order and hand back three variants with counts. If the scoring rubric in aso-audit does not match your own view of what matters, you are adopting someone else's opinion, and you should read the file before you install it.

The router and the direct invocation paths

Thirty-odd skills is too many to hold in your head, and the README acknowledges that with a router. The /aso-router command takes a request and routes it to the right specialist skill. Direct invocation is also documented: /aso-audit, /keyword-research, /metadata-optimization, /market-movers, /market-pulse, /asc-metrics, /in-app-events, /seasonal-aso, /android-aso, /apple-search-ads, /competitor-tracking. The router is the more interesting design choice. It implies a classification step in front of every request, which means a misrouted request gets a confidently wrong skill applied to it. For a request like "optimize my Google Play listing" the routing is obvious. For something like "my rating dropped after the last release", the correct skill could plausibly be review-management, android-aso, or aso-audit, and the router has to pick. That is a place where the tool can quietly give you the wrong framework. Naming the skill directly is the safer habit when you already know which framework you want.

Installing it and the commands the README gives

Cursor: npx skills add eronred/aso-skills -a cursor. Claude Code: npx skills add eronred/aso-skills, with no agent flag. Manual install: git clone https://github.com/eronred/aso-skills.git followed by cp -r aso-skills/skills/* .cursor/skills/. Note the manual path. It copies into .cursor/skills/ specifically, so if you are on Claude Code and installing manually, the destination is not the one shown. The README does not spell out the Claude Code manual destination, which is a real gap for anyone who cannot or will not run npx. Once installed, the usage pattern is plain language with an app identifier attached, for example: "Run an ASO audit for my app (id: 1617391485)". The numeric App Store ID is how the agent connects a request to a specific listing. Other documented prompts include "Find the best keywords for a meditation app", "How many downloads do I need to reach top 10 in Health & Fitness?", and "How are my downloads and revenue trending this month?". The last one is worth pausing on. Download and revenue trends require App Store Connect access, which is a different data path from public App Store lookups, and the README does not explain how that credential is supplied.

The Appeeky dependency is the real constraint

The repository describes itself as powered by real App Store data via the Appeeky API, and the homepage points at appeeky.com. That is the load-bearing sentence in the whole README. The skill files supply the reasoning; Appeeky supplies the numbers. Without working API access, the agent is back to writing plausible-sounding ASO copy from priors, which is exactly the generic advice the project says it exists to replace. The README does not document rate limits, pricing, authentication setup, or what happens when the API call fails. It links to docs.appeeky.com for the API detail, and that is where you have to go before you can judge whether the setup is viable for your volume. There is a second layer here worth naming: the same README promotes a native macOS app that syncs App Store Connect, Google Play, Apple Search Ads, Meta and TikTok ads, and RevenueCat into a local cache, with credentials in the Keychain and a built-in terminal that pairs with MCP so the same agents can talk to local data. So the open repository and the commercial product are designed to fit together. That is not a criticism, but it does mean the free MIT skills are the entry point to a paid data and desktop layer, and you should evaluate them as such rather than as a self-contained tool.

Where this approach breaks down

Skill files are instructions, and instructions are interpreted. Two runs of the same prompt against the same skill can produce different titles, different keyword groupings, and different scores. If your workflow requires a change log you can diff and a listing you can reproduce exactly, this is the wrong shape of tool. The second failure mode is scope creep inside a single skill. The README lists a skill for triaging a crash affecting 2 percent of sessions, and another for pitching TechCrunch. Those are app growth tasks, not ASO tasks, and bundling them into the same repository means the router has more opportunities to send a request to a framework that does not fit it. The third is staleness. App Store rules change: keyword field length, screenshot slot counts, In-App Event requirements, Custom Product Page behaviour. A skill file that encodes a specific rule is only as current as the last push. The repository shows activity as of August 2026, but nothing in the material describes a versioning or deprecation policy for individual skills, so you cannot tell from the outside which files were touched recently. If you depend on a skill for a rule-bound task, check the file's own content rather than assuming it tracks current App Store policy.

How it differs from ASO tools with a UI

The obvious alternative is a hosted ASO platform where you type a keyword into a search box and read a difficulty score off a table. The difference in approach is where the reasoning lives. A hosted tool encodes its judgement in a fixed interface: the columns it shows you are the dimensions it believes matter, and you cannot add an eleventh. aso-skills encodes judgement in editable markdown, so the ten factors in aso-audit are text you can read, argue with, and fork. That is the genuine advantage, and it is also the cost. A hosted tool gives you the same answer for the same input every time. A skill file gives you a framework and lets the model fill it in. For a solo developer who wants a consistent weekly audit of one listing, the hosted tool is less work. For a team that wants its own ASO doctrine applied by an agent that also has access to the codebase, the skill-file approach is the one that lets you write the doctrine down. The MIT licence is what makes the second option real: you can fork the repository and edit the rubrics without asking anyone.

Maintenance, licence, and what to check before installing

The licence is MIT, which permits commercial use, modification, and redistribution provided the copyright notice and permission notice are retained. That is the standard permissive arrangement and it means you can fork the skills into a private repository and adapt the rubrics to your own category. It does not cover the Appeeky API, which is a separate service with its own terms, and it does not cover the App Store data itself. Nothing here is legal advice; read the LICENSE file in the repository and the Appeeky terms separately. On maintenance: there are no retrieved releases, so there is no tagged version to pin. If you install via npx skills add, you get whatever is on main at that moment, and a later update can change a rubric underneath you. The practical mitigation is to clone rather than npx-install if you care about stability, keep your own copy, and diff it against upstream when you want to pull changes. Before you install, read skills/aso-audit and check whether its ten factors and its 0 to 100 scale match how you would actually rank a listing. If they do not, you are about to hand your ASO decisions to a rubric you have not read.

Editorial conclusion

Adopt aso-skills if you already run Cursor or Claude Code, you are comfortable installing skills from a third-party repository, and you accept that live data comes from Appeeky. Do not adopt it if your ASO work is a spreadsheet-and-console routine that does not need an agent in the loop, or if you need guaranteed reproducible output that you can diff between runs. Verify first: read the aso-audit skill file and confirm the ten scoring factors match how you actually judge a listing, check what the Appeeky API returns and under what terms, and confirm the skill directory lands where your agent looks for it, since the manual path in the README is .cursor/skills/.

Official sources

1. Eronred/aso-skills on GitHub
2. Issues
3. License: MIT
4. Project website
5. README