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.
npx agentmods add skills/robertguss/claude-code-toolkit/app-store-listing-optimizernpx skills add robertguss/claude-code-toolkit --skill app-store-listing-optimizergit clone --depth 1 https://github.com/robertguss/claude-code-toolkitWrote 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.
[](https://agentmods.dev/skills/robertguss/claude-code-toolkit/app-store-listing-optimizer)<a href="https://agentmods.dev/skills/robertguss/claude-code-toolkit/app-store-listing-optimizer"><img src="https://agentmods.dev/badge/skills/robertguss/claude-code-toolkit/app-store-listing-optimizer.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00137 | $0.02755 |
| Opus 5 | $0.00068 | $0.01378 |
| Sonnet 5 | $0.00027 | $0.00551 |
| Haiku 4.5 | $0.00014 | $0.00276 |
Grade A, and why
app-store-listing-optimizer 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Store Listing Optimizer
Craft high-converting, keyword-optimized App Store and Google Play listings through competitive research, strategic keyword selection, and data-driven screenshot planning.
Prerequisites
- Chrome browser with Claude in Chrome extension (for browsing competitor listings)
- No API keys required — all research uses live store browsing
- Supports iOS App Store, Google Play Store, or both
Workflow Overview
1. App Analysis — Understand the app, audience, differentiators
2. Competitive Research — Browse competitor listings, extract keywords
3. Keyword Selection — Identify high-intent, low-competition keywords
4. Craft the Listing — Write optimized metadata for each platform
5. Screenshot Strategy — Plan visual sequence and caption copy
6. A/B Test Variants — Generate 2-3 alternatives for split testing
Step 1: Analyze the App
Gather everything needed to position the app effectively. Ask the user:
- What does the app do? One sentence, plain language.
- Target platform? iOS, Android, or both?
- Who is it for? Primary audience, age range, expertise level.
- Top 3 features — What does it do better or differently?
- Differentiator — Why pick this over competitors?
- Monetization — Free, freemium, subscription, one-time purchase?
- Current listing (if updating) — Share the existing store URL.
Document the answers in a structured brief before proceeding.
Step 2: Competitive Keyword Research
Browse 8-12 competitor listings on the target store(s) using Chrome. For detailed methodology and search patterns, see references/keyword-research.md.
What to Extract Per Competitor
| Field | Where to Find |
|---|---|
| App name | Title on store listing |
| Subtitle / short | Below title (iOS) or short desc (Android) |
| Full description | Store listing body |
| Rating + count | Store listing header |
| Category rank | Store listing or chart position |
| Screenshots | Visual carousel — note caption text |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 326 lines · 137 tokens per session scan A e438ceb30109
app-store-listing-optimizer is a skill published in the GitHub repository robertguss/claude-code-toolkit (108 stars, last pushed 26d ago), licensed MIT. It adds 137 tokens to every session and 2,755 once invoked, about $0.0007 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 2026-08-30.
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