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/cgyudistira/agentkit/app-store-optimizationnpx skills add cgyudistira/agentkit --skill app-store-optimizationgit clone --depth 1 https://github.com/cgyudistira/agentkitWrote 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/cgyudistira/agentkit/app-store-optimization)<a href="https://agentmods.dev/skills/cgyudistira/agentkit/app-store-optimization"><img src="https://agentmods.dev/badge/skills/cgyudistira/agentkit/app-store-optimization.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 | $0.00032 | $0.03564 |
| Opus 5 | $0.00016 | $0.01782 |
| Sonnet 5 | $0.00006 | $0.00713 |
| Haiku 4.5 | $0.00003 | $0.00356 |
Grade A, and why
app-store-optimization 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 3d 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.
This is a copy
100% identical to app-store-optimization — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 404 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Store Optimization (ASO) Skill
This comprehensive skill provides complete ASO capabilities for successfully launching and optimizing mobile applications on the Apple App Store and Google Play Store.
Capabilities
Research & Analysis
- Keyword Research: Analyze keyword volume, competition, and relevance for app discovery
- Competitor Analysis: Deep-dive into top-performing apps in your category
- Market Trend Analysis: Identify emerging trends and opportunities in your app category
- Review Sentiment Analysis: Extract insights from user reviews to identify strengths and issues
- Category Analysis: Evaluate optimal category and subcategory placement strategies
Metadata Optimization
- Title Optimization: Create compelling titles with optimal keyword placement (platform-specific character limits)
- Description Optimization: Craft both short and full descriptions that convert and rank
- Subtitle/Promotional Text: Optimize Apple-specific subtitle (30 chars) and promotional text (170 chars)
- Keyword Field: Maximize Apple's 100-character keyword field with strategic selection
- Category Selection: Data-driven recommendations for primary and secondary categories
- Icon Best Practices: Guidelines for designing high-converting app icons
- Screenshot Optimization: Strategies for creating screenshots that drive installs
- Preview Video: Best practices for app preview videos
- Localization: Multi-language optimization strategies for global reach
Conversion Optimization
- A/B Testing Framework: Plan and track metadata experiments for continuous improvement
- Visual Asset Testing: Test icons, screenshots, and videos for maximum conversion
- Store Listing Optimization: Comprehensive page optimization for impression-to-install conversion
- Call-to-Action: Optimize CTAs in descriptions and promotional materials
Rating & Review Management
- Review Monitoring: Track and analyze user reviews for actionable insights
- Response Strategies: Templates and best practices for responding to reviews
- Rating Improvement: Tactical approaches to improve app ratings organically
- Issue Identification: Surface common problems and feature requests from reviews
What ships with it
12 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.
- ab_test_planner.py 22 KB runs code
- aso_scorer.py 19 KB runs code
- competitor_analyzer.py 21 KB runs code
- expected_output.json 5.4 KB
- HOW_TO_USE.md 10 KB
- keyword_analyzer.py 13 KB runs code
- launch_checklist.py 28 KB runs code
- localization_helper.py 22 KB runs code
- metadata_optimizer.py 20 KB runs code
- README.md 15 KB
- review_analyzer.py 25 KB runs code
- sample_input.json 723 B
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.
- 3d ago First seen · 404 lines · 32 tokens per session scan A 246f6f1624fe
app-store-optimization is a skill published in the GitHub repository cgyudistira/agentkit (2 stars, last pushed 6mo ago), licensed MIT. It adds 32 tokens to every session and 3,564 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to app-store-optimization, differing in 0 lines, and is treated as a copy.
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