home / skills / vladimirbrejcha / ios-ai-skills / app-store-optimisation
This skill conducts end-to-end App Store and Play Store optimization analyses, delivering keyword research, metadata recommendations, and launch planning to
npx playbooks add skill vladimirbrejcha/ios-ai-skills --skill app-store-optimisationReview the files below or copy the command above to add this skill to your agents.
---
name: app-store-optimisation-codex
description: App Store Optimization (ASO) workflows for Apple App Store and Google Play Store. Use when Codex is asked to research keywords, optimize app metadata (titles, subtitles, descriptions, keywords), analyze competitors, plan A/B tests, compute ASO scores, analyze reviews, plan localization, or build launch/update checklists for mobile apps.
---
# App Store Optimisation (Codex)
## Overview
Provide end-to-end ASO support for App Store and Play Store listings, from research to execution. Use bundled Python modules for structured analysis and planning, and use browsing or user-provided inputs for live data.
## Quick Start
1. Identify the task: keyword research, metadata optimization, competitor analysis, review analysis, ASO scoring, A/B testing, localization, or launch planning.
2. Request required inputs (platforms, markets, current metadata, target keywords, metrics).
3. Use the matching script from `scripts/` to generate analysis or plans.
4. Validate character limits and platform rules, then deliver actionable recommendations.
## Core Tasks
### Keyword research
Use `scripts/keyword_analyzer.py`.
Request:
- Candidate keywords
- Estimated search volume and competition (user-provided or inferred)
- Relevance score per keyword
Deliver:
- Ranked keywords (primary, secondary, long-tail)
- Difficulty and potential scores
### Metadata optimisation
Use `scripts/metadata_optimizer.py`.
Request:
- Current metadata (title, subtitle, descriptions)
- Target keywords and value proposition
- Platform(s) and markets
Deliver:
- Optimized titles and descriptions with character counts
- Keyword density guidance
Default character limits (verify current limits before final output):
- Apple App Store: title 30, subtitle 30, promo text 170, description 4000, keywords 100
- Google Play: title 50, short description 80, full description 4000
### Competitor analysis
Use `scripts/competitor_analyzer.py`.
Request:
- Competitor names or IDs
- Platform and market
Optional data collection:
- Use `scripts/itunes_api.py` for Apple metadata
- Use browsing with prompt templates from `scripts/scraper.py`
Deliver:
- Keyword overlap, metadata patterns, visual asset notes, and gaps
### Review analysis
Use `scripts/review_analyzer.py`.
Request:
- Review text, ratings, and date range
Deliver:
- Sentiment split, top issues, feature requests, response templates
### ASO scoring
Use `scripts/aso_scorer.py`.
Request:
- Metadata quality inputs, ratings data, keyword ranking counts, conversion metrics
Deliver:
- Overall score with category breakdown and prioritized recommendations
### A/B testing
Use `scripts/ab_test_planner.py`.
Request:
- Baseline conversion rate and traffic
- Variants and test goal
Deliver:
- Sample size, duration guidance, and success metrics
### Localization planning
Use `scripts/localization_helper.py`.
Request:
- Current markets and target locales
- Budget and priority markets
Deliver:
- Localization priority order and draft localized metadata
### Launch and update checklists
Use `scripts/launch_checklist.py`.
Request:
- Platform(s), launch date, category, and key features
Deliver:
- Pre-launch checklist and post-launch monitoring plan
## Data Sources
See `references/data_sources.md` for API and browsing guidance.
## Resources
### scripts/
- `keyword_analyzer.py`
- `metadata_optimizer.py`
- `competitor_analyzer.py`
- `review_analyzer.py`
- `aso_scorer.py`
- `ab_test_planner.py`
- `localization_helper.py`
- `launch_checklist.py`
- `itunes_api.py`
- `scraper.py`
### references/
- `data_sources.md`
- `sample_input.json`
- `expected_output.json`
This skill provides end-to-end App Store Optimization (ASO) workflows for Apple App Store and Google Play Store listings. It helps research keywords, optimize metadata, analyze competitors and reviews, plan A/B tests, compute ASO scores, and prepare localization and launch/update checklists. Use it to turn data and market signals into prioritized, actionable recommendations.
The skill inspects supplied metadata, candidate keywords, competitor listings, review text, and performance metrics to produce structured analysis and plans. It uses bundled Python modules for keyword ranking, metadata optimization, competitor and review analysis, ASO scoring, A/B test planning, localization prioritization, and checklist generation. Where needed, it can incorporate live or user-provided market data, then validates outputs against platform character limits and rules.
Do outputs respect platform character limits?
Yes. The skill validates against common limits and flags areas to verify against the latest App Store and Play Store rules before publishing.
Can it plan localization across many markets?
Yes. It prioritizes locales by impact and cost and can draft localized metadata to accelerate translation and review.