app-store-optimization
App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on…
Skill by fernandogarzaaa
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: testing, marketing, research
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What's inside
- Skill name
app-store-optimization- Description
- App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking changes.
- License
- MIT
- Instructions
- 470 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (17)
- HOW_TO_USE.md
- README.md
- SKILL.md
- aso-audit-template.md · assets/
- expected_output.json
- aso-best-practices.md · references/
- keyword-research-guide.md · references/
- platform-requirements.md · references/
- sample_input.json
- ab_test_planner.py · scripts/
- aso_scorer.py · scripts/
- competitor_analyzer.py · scripts/
- keyword_analyzer.py · scripts/
- launch_checklist.py · scripts/
- localization_helper.py · scripts/
- metadata_optimizer.py · scripts/
- review_analyzer.py · scripts/
SKILL.md (first lines)
# App Store Optimization (ASO)
---
## Keyword Research Workflow
Discover and evaluate keywords that drive app store visibility.
### Workflow: Conduct Keyword Research
1. Define target audience and core app functions:
- Primary use case (what problem does the app solve)
- Target user demographics
- Competitive category
2. Generate seed keywords from:
- App features and benefits
- User language (not developer terminology)
- App store autocomplete suggestions
3. Expand keyword list using:
- Modifiers (free, best, simple)
- Actions (create, track, organize)
- Audiences (for students, for teams, for business)
4. Evaluate each keyword:
- Search volume (estimated monthly searches)
- Competition (number and quality of ranking apps)
- Relevance (alignment with app function)
5. Score and prioritize keywords:
- Primary: Title and keyword field (iOS)
- Secondary: Subtitle and short description
- Tertiary: Full description only
6. Map keywords to metadata locations
7. Document keyword strategy for tracking
8. **Validation:** Keywords scored; placement mapped; no competitor brand names included; no plurals in iOS keyword field
### Keyword Evaluation Criteria
| Factor | Weight | High Score Indicators |
|--------|--------|----------------------|
| Relevance | 35% | Describes core app function |
| Volume | 25% | 10,000+ monthly searches |
| Competition | 25% | Top 10 apps have <4.5 avg rating |
| Conversion | 15% | Transactional intent ("best X app") |
### Keyword Placement Priority
| Location | Search Weight |
|----------|---------------|
| App Title | Highest |
| Subtitle (iOS) | High |
| Keyword Field (iOS) | High |
| Short Description (Android) | High |
| Full Description | Medium |
See: [references/keyword-research-guide.md](references/keyword-research-guide.md)
---
## Metadata Optimization Workflow
Optimize app store listing elements for search ranking and conversion.
### Workflow: Optimize App Metadata
1. Audit current metadata against platform limits:
- Title character count and keyword presence
- Subtitle/short description usage
- Keyword field efficiency (iOS)
- Description keyword density
2. Optimize title following formula:
```
[Brand Name] - [Primary Keyword] [Secondary Keyword]
```
3. Write subtitle (iOS) or short description (Android):
- Focus on primary benefit
- Include secondary keyword
- Use action verbs
4. Optimize keyword field (iOS only):
- Remove duplicates from title
- Remove plurals (Apple indexes both forms)
- No spaces after commas
- Prioritize by score
5. Rewrite full description:
- Hook paragraph with value proposition
- Feature bullets with keywords
- Social proof section
- Call to action
6. Validate character counts for each field
7. Calculate keyword density (target 2-3% primary)
8. **Validation:** All fields within character limits; primary keyword in title; no keyword stuffing (>5%); natural language preserved
### Platform Character Limits
| Field | Apple App Store | Google Play Store |
|-------|-----------------|-------------------|
| Title | 30 characters | 50 characters |
| Subtitle | 30 characters | N/A |
| Short Description | N/A | 80 characters |
| Keywords | 100 characters | N/A |
| Promotional Text | 170 characters | N/A |
| Full Description | 4,000 characters | 4,000 characters |
| What's New | 4,000 characters | 500 characters |
### Description Structure
```
PARAGRAPH 1: Hook (50-100 words)
├── Address user pain point
├── State main value proposition
└── Include primary keyword
PARAGRAPH 2-3: Features (100-150 words)
├── Top 5 features with benefits
├── Bullet points for scanability
└── Secondary keywords naturally integrated
PARAGRAPH 4: Social Proof (50-75 words)
├── Download count or rating
├── Press mentions or awards
└── Summary of user testimonialsFrom fernandogarzaaa/OpenChimera_v1, MIT.
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Signals
- Stars
- 4
- Forks
- 0
- Open issues
- 1
- Last commit (this folder)
- Mar 20, 2026 (7 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
app-store-optimization skill. Agent Mart. https://agentmart.dev/skills/fernandogarzaaa-app-store-optimization (last updated 2026-10-24)