app-store-toolkit:competitors

app-store-toolkit:competitors is a skill for Claude Code from vishalvshekkar/app-store-toolkit. It costs 17 tokens per session (756 once invoked), scanned A, original, MIT.

A competitor-research guide for Apple App Store listings and ASO, or app-store search optimization. It compares competing apps’ names, subtitles, descriptions, keywords, ratings, categories, and rankings.

In plain words
What is it for?
Use it to research three to five competing apps, compare listing language, and identify keyword gaps. It can also compare the findings with your current local app metadata when available.
Why use it?
It helps reveal how similar apps describe themselves and which search terms or listing details may be missing from your own listing.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

Part of the app-store-toolkit plugin — 19 skills, 2 agents, 2 hooks, 1 MCP server shipped together

Good fit Use it to research three to five competing apps, compare listing language, and identify keyword gaps. It can also compare the findings with your current local app metadata when available.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vishalvshekkar/app-store-toolkit/competitors
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 vishalvshekkar/app-store-toolkit --skill competitors
Clone the repo
git clone --depth 1 https://github.com/vishalvshekkar/app-store-toolkit

Made for: Claude Code.

Or install app-store-toolkit, the plugin that ships this one along with the rest of its 19 skills, 2 agents, 2 hooks, 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 app-store-toolkit:competitors

README.md
[![agentmods](https://agentmods.dev/badge/skills/vishalvshekkar/app-store-toolkit/competitors.svg)](https://agentmods.dev/skills/vishalvshekkar/app-store-toolkit/competitors)
Your own site
<a href="https://agentmods.dev/skills/vishalvshekkar/app-store-toolkit/competitors"><img src="https://agentmods.dev/badge/skills/vishalvshekkar/app-store-toolkit/competitors.svg" alt="Measured on agentmods" height="20"></a>
Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 756 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 $0.00017 $0.00756
Opus 5 $0.00009 $0.00378
Sonnet 5 $0.00003 $0.00151
Haiku 4.5 $0.00002 $0.00076

Measured 8d ago against content hash 3af13841adc5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

app-store-toolkit:competitors 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 8d 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.

skills/competitors/SKILL.md · 96 lines

How it starts

The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/app-store-toolkit:competitors

You are analyzing competitor App Store listings to provide ASO insights.

Steps

1. Understand the Request

Parse $ARGUMENTS to determine what competitors to analyze. The user might provide:

  • A search term (e.g., "productivity timer")
  • A specific app name (e.g., "Forest")
  • A category (e.g., "Productivity")

1b. Load Current Metadata (Optional)

Call store_read_config and store_read_metadata to load the user's existing metadata for comparison. If no config exists, that's OK — competitor analysis can still run without local metadata, but keyword gap analysis will be skipped.

2. Research Competitors

Use web search to find competitor App Store listings. Search for:

  • site:apps.apple.com "{query}" to find specific apps
  • "{query}" app store top apps for category research
  • Look at Apple's App Store search results for the query

For each competitor (analyze 3-5 top competitors):

  • App name and subtitle
  • Key features highlighted in description
  • Keywords used (inferred from name, subtitle, description)
  • Rating and review count
  • Category and ranking position

3. Keyword Gap Analysis

Compare competitors' keyword strategies with the user's current metadata:

  • Read user's current metadata via store_read_config and store_read_metadata
  • Identify keywords competitors use that the user doesn't
  • Identify unique keywords the user has that competitors don't
  • Suggest high-value keywords to add

4. Present Analysis

Competitor Analysis: "productivity timer"
═══════════════════════════════════════════════════

1. Forest — Stay Focused (4.8 stars, 500K+ ratings)
   Name Strategy: Brand + clear benefit
   Keywords observed: focus, study, timer, productivity, pomodoro
   Strength: Strong emotional branding, gamification angle

2. Focus Timer — Pomodoro (4.6 stars, 100K+ ratings)
   Name Strategy: Feature-first + technique name
   Keywords observed: pomodoro, focus, timer, study, concentration
   Strength: Direct keyword match in name

3. Be Focused — Focus Timer (4.5 stars, 50K+ ratings)
   Name Strategy: Action verb + feature
   Keywords observed: focus, timer, productivity, work, break
   Strength: Action-oriented naming

Keyword Gaps (terms competitors use that you don't):
  - pomodoro (3/3 competitors)
  - study (2/3 competitors)
  - concentration (2/3 competitors)

Your Unique Keywords:
  - organize, planning, goals

Recommendations:
  1. Add "pomodoro" to keywords — high search volume, all competitors use it
  2. Consider "study" — captures student demographic
  3. Your name could benefit from a clearer benefit descriptor
═══════════════════════════════════════════════════

Read the full file on GitHub · 96 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. 8d ago First seen · 96 lines · 17 tokens per session scan A 3af13841adc5

Subscribe to this mod's changes

app-store-toolkit:competitors is a skill published in the GitHub repository vishalvshekkar/app-store-toolkit (5 stars, last pushed 3mo ago), licensed MIT. It adds 17 tokens to every session and 756 once invoked, about $0.0001 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 2026-08-31.

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