app-store-optimization

App Store Optimization (ASO)

ASO tools for researching keywords, optimizing metadata, analyzing competitors, and improving app store visibility on Apple App Store and Google Play Store. This file is a lean map — execute a task by loading the matching reference below.

Core Capabilities

• Keyword research — seed/expand/score keywords by relevance, volume, competition, and conversion intent; map to metadata placements
• Metadata optimization — title, subtitle/short description, iOS keyword field, and full description against platform character limits and density targets
• Competitor analysis — keyword matrices, gap analysis, visual and ratings benchmarking across the top 10 competitors
• Launch & A/B testing — structured launch checklists, timing, and conversion experiments with sample-size and significance math
• Reviews & localization — sentiment/theme/issue extraction and multi-market metadata adaptation
• 8 Python tools — keyword_analyzer, metadata_optimizer, competitor_analyzer, aso_scorer, ab_test_planner, review_analyzer, launch_checklist, localization_helper (stdlib only, analyze data you provide)

When to Use

• Researching or scoring keywords for an app store listing
• Optimizing a title/subtitle/description/keyword field for ranking and conversion
• Auditing competitors for keyword gaps and positioning opportunities
• Planning an app launch or running a store-listing A/B test
• Analyzing reviews or planning multi-market localization

Clarify First

Before generating, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Platform — Apple App Store vs Google Play (different character limits, keyword field vs description indexing, ranking factors)
• App category + seed keywords — the app's space and starting terms (drives keyword research + scoring)
• Primary goal — ranking visibility vs conversion rate (shapes metadata, title/subtitle, and screenshot priorities)
• Target market/locale — which storefronts (drives localization + keyword volume estimates)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

python scripts/keyword_analyzer.py --keywords "todo,task,planner"
python scripts/metadata_optimizer.py --platform ios --title "App Title"
python scripts/aso_scorer.py --app-id com.example.app

Note: the scripts are importable Python libraries — see the Tool Reference for classes, methods, and convenience functions.

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/aso-workflows.md — step-by-step procedures, scoring criteria, placement tables, structure diagrams, templates, and before/after examples for all five workflows. Read when executing keyword research, metadata optimization, competitor analysis, launch, or A/B testing.
• references/tool-reference.md — full usage for the 8 Python scripts: classes, methods, parameters, returns, convenience functions, plus the scripts and assets tables. Read before invoking a tool.
• references/operations-and-benchmarks.md — troubleshooting table, success criteria/targets, platform limitations, proactive triggers, output artifacts, communication standards, related skills, and the full integration matrix. Read when diagnosing issues, setting targets, or wiring into other tools.
• references/keyword-research-guide.md — research methodology, evaluation framework, and tracking. Read for deep keyword discovery and selection.
• references/platform-requirements.md — iOS and Android metadata specs and visual asset requirements. Read when validating fields against platform rules.
• references/aso-best-practices.md — optimization strategies, rating management, and launch tactics. Read for proven tactics and playbooks.

Scope & Limitations

In scope: keyword research, metadata optimization and character-limit validation, competitor ASO analysis (public data), A/B test planning with significance math, launch/seasonal/localization planning, and review sentiment analysis for Apple App Store and Google Play Store.

Out of scope: real-time store data fetching (scripts analyze static data you provide), Apple Ads (formerly Apple Search Ads) / Google Ads campaign management, creative asset design, cross-device attribution (use an MMP), in-app analytics/retention, and revenue/subscription pricing.

Data constraints: no official search-volume API exists for either store (estimates use third-party tools or heuristics); competitor and review data are limited to public info; historical ranking data needs external tools (AppTweak, Sensor Tower, data.ai); Apple's June 2025 update indexes screenshot text, which these scripts do not yet analyze. See references/operations-and-benchmarks.md for details.

Integration Points

Connects to Apple App Store Connect and Google Play Console (metadata submission, Product Page Optimization / Store Listing Experiments), Apple Ads (formerly Apple Search Ads; keyword discovery), ASO tools (AppTweak, Sensor Tower, data.ai for volume/ranking data), analytics (Firebase/Mixpanel/Amplitude for engagement signals), and the campaign-analytics and content-creator skills. Full connection details and data flows: references/operations-and-benchmarks.md.