Gemini Aso

Gemini ASO

This skill provides specialized tools and workflows for optimizing mobile applications for discoverability and conversion on major app stores.

Core Capabilities

1. Keyword Research & Analysis

Analyze search volume, competition, and relevance to identify high-potential keywords.

• Tool: scripts/keyword_analyzer.py
• Output: Ranked keywords, difficulty scores, and long-tail opportunities.

2. Metadata Optimization

Generate and validate store listings (Title, Description, Keywords) against platform-specific constraints.

• Tool: scripts/metadata_optimizer.py
• Constraints: Automatically handles Apple (30/30/170/4000) and Google (50/80/4000) limits.

3. Competitor Intelligence

Deep-dive into top-performing apps to identify gaps and strategy overlaps.

• Tool: scripts/competitor_analyzer.py
• Integration: Uses scripts/lib/itunes_api.py for real-time Apple data.

4. Performance Scoring

Calculate an overall ASO health score (0-100) based on metadata quality, ratings, and keyword performance.

• Tool: scripts/aso_scorer.py
• Context: Supports competitive, niche, and utility benchmarks.
• Platform: Platform-aware weighting for apple vs google.

5. Reporting & Dashboards

Generate human-readable ASO audits and market overviews.

• Formats: Supports --format markdown for visual dashboards.
• Logic: Automated recommendation prioritization.

Specialized Workflows

• Review Analysis: Extract sentiment and feature requests from user feedback via scripts/review_analyzer.py.
• A/B Testing: Design metadata experiments with scripts/ab_test_planner.py.
• Global Reach: Plan localization strategies using scripts/localization_helper.py.

Quick Start

Perform a Formatted ASO Audit

{
  "metadata": {"title_length": 30, "description_length": 2500},
  "ratings": {"average_rating": 4.5, "total_ratings": 1000},
  "keyword_performance": {"top_10": 5},
  "conversion": {"impression_to_install": 0.05},
  "platform": "apple",
  "category_context": "competitive"
}

Run python3 scripts/aso_scorer.py --format markdown < input.json to get a visual health dashboard.

Competitive Market Analysis

Ask Gemini: "Analyze the Productivity market on the App Store. Show me a formatted report with competitor rankings and keyword gaps." Gemini will use scripts/competitor_analyzer.py --format markdown to generate the report.

Resources

scripts/

• keyword_analyzer.py: Main keyword engine.
• metadata_optimizer.py: Field-specific optimization and validation.
• aso_scorer.py: Comprehensive health scoring.
• competitor_analyzer.py: Market analysis tool.
• lib/: Contains itunes_api.py (official API wrapper) and scraper.py (WebFetch utilities).

references/

• config.json: Master Configuration. Contains all genre mappings and category-specific benchmarks (Competitive, Niche, Utility).
• sample_input.json: Template for tool inputs.
• expected_output.json: Schema for tool responses.
• agents.md: Documentation on agent-coordinated workflows.

Best Practices

1. API First: Always use the iTunesAPI for Apple data before falling back to scraping.
2. Context Matters: Specify a category_context (e.g., competitive) for more accurate scoring and tailored recommendations.
3. Go Global: Use the country parameter for international market research (supports all ISO codes).
4. Markdown for Humans: Use the --format markdown flag when you need a visual summary for the user.
5. Validate: Always run character count validation before proposing metadata changes.
6. Singular Only: Use singular forms in Apple's 100-character keyword field; plurals are auto-indexed.