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aso-research

aso-research is a skill for Claude Code from applyra-io/mcp-server. It costs 119 tokens per session (1,456 once invoked), scanned A, original, MIT.

A research workflow for improving how an app appears in the Apple App Store or Google Play. ASO, or app-store optimization, means improving an app's keywords and store listing so it can be found more easily.

In plain words
What is it for?
Use it to inspect keyword rankings, find keyword ideas, compare competing apps, assess listing health, and simulate changes to titles, descriptions, subtitles, or keywords.
Why use it?
It helps explain ranking changes, find search terms, compare competitors, and check proposed store text before publishing it. This replaces guesswork with keyword and metadata research.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the applyra plugin — 1 skill, 1 MCP server shipped together

not rated 0repo changed 9d ago A scan 119 tokens original MIT

Good fit Use it to inspect keyword rankings, find keyword ideas, compare competing apps, assess listing health, and simulate changes to titles, descriptions, subtitles, or keywords.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/applyra-io/mcp-server/aso-research
View source ↗ applyra-io/mcp-server applyra.io
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 applyra-io/mcp-server --skill aso-research
Clone the repo
git clone --depth 1 https://github.com/applyra-io/mcp-server

Made for: Claude Code.

Or install applyra, the plugin that ships this one along with the rest of its 1 skill, 1 MCP server.

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 aso-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/applyra-io/mcp-server/aso-research/github.svg)](https://agentmods.dev/skills/applyra-io/mcp-server/aso-research)
Your own site
<a href="https://agentmods.dev/skills/applyra-io/mcp-server/aso-research"><img src="https://agentmods.dev/badge/skills/applyra-io/mcp-server/aso-research/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 aso-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/applyra-io/mcp-server/aso-research"><img src="https://agentmods.dev/badge/skills/applyra-io/mcp-server/aso-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 119 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,456 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 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 5.1 $0.00119 $0.01456
Opus 5.5 $0.00048 $0.00582
Sonnet 5.5 $0.00024 $0.00291
Haiku 4.5 $0.00012 $0.00146

Measured 9d ago against content hash 5361344babe8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

aso-research 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 9d ago.

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.

claude-plugin/skills/aso-research/SKILL.md · 95 lines

How it starts

The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ASO research with Applyra

Applyra tracks the user's apps on the App Store and Google Play every day. Its tools read that data, and score listing text on the same engine that audits live listings. No tool publishes anything to App Store Connect or Google Play: the user ships changes themselves.

Ground rules

  • Start from list_applications. It gives each app's numeric id, which every app-scoped tool takes, with its store, country and language. Never guess an ID. add_application and add_competitor are the exceptions: they take the store bundle ID.
  • Every number comes from a tool call. Say which tool it came from, with the store, the country and the date of the data. Quote difficulty and traffic as the 0-100 scores Applyra returns.
  • Rankings are per store and per country. Keep markets apart, never blend them into one figure.
  • Let check_metadata count lengths. The stores count UTF-16 code units, not characters, so a count done by hand is wrong in many languages.
  • Ask before changing the account. track_keywords, untrack_keyword, set_keyword_favorite, add_application, add_competitor and remove_competitor change what Applyra tracks, and add_note, update_note and delete_note change the notes on its charts. Every other tool only reads.
  • Reuse past work first. list_keyword_inspections, list_niche_analyses, list_metadata_simulations and list_autocomplete_history return what was already run.

What moved

  1. list_keywords for the app: current rank, the apps just ahead and behind, the top 5.
  2. get_keyword_rank_history on the keywords that moved, with the keyword_id from list_keywords. It covers the last 30 days unless a range is given.
  3. Report before and after, largest drops first. A move of one or two places is daily noise: lead with the large ones.
  4. When many keywords fell on the same day, get_app_score_history shows whether the app's overall visibility fell with them. A drop across the board on one date usually comes from the store, not from the listing: its events.store_updates list the store algorithm updates of the market.

Read the full file on GitHub · 95 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. 9d ago Changed · +11 lines 5361344babe8
  2. 14d ago Changed · +60 lines · +68 tokens per session 285f35925ad9
  3. 22d ago First seen · 24 lines · 51 tokens per session scan A 7e923d6d414c

Subscribe to this mod's changes

aso-research is a skill published in the GitHub repository applyra-io/mcp-server (0 stars, last pushed 10d ago), licensed MIT. It adds 119 tokens to every session and 1,456 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-09-30.

Marketing and SEOResearchapp-storeapp-store-optimizationasoclaudegoogle-playmcp
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