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:
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 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 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.
1---2name: app-store-optimization3description: App Store Optimization toolkit for researching keywords, optimizing metadata, and tracking mobile app performance on Apple App Store and Google Play Store.4license: MIT + Commons Clause5---6# App Store Optimization (ASO)
7
8ASO 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.
9
10## Core Capabilities
11
12- **Keyword research** — seed/expand/score keywords by relevance, volume, competition, and conversion intent; map to metadata placements
13- **Metadata optimization** — title, subtitle/short description, iOS keyword field, and full description against platform character limits and density targets
14- **Competitor analysis** — keyword matrices, gap analysis, visual and ratings benchmarking across the top 10 competitors
15- **Launch & A/B testing** — structured launch checklists, timing, and conversion experiments with sample-size and significance math
16- **Reviews & localization** — sentiment/theme/issue extraction and multi-market metadata adaptation
17- **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)
18
19## When to Use
20
21- Researching or scoring keywords for an app store listing
22- Optimizing a title/subtitle/description/keyword field for ranking and conversion
23- Auditing competitors for keyword gaps and positioning opportunities
24- Planning an app launch or running a store-listing A/B test
25- Analyzing reviews or planning multi-market localization
26
27## Clarify First
28
29Before generating, confirm these inputs. If any is unknown or vague, ASK — do not assume:
30
31- [ ] **Platform** — Apple App Store vs Google Play (different character limits, keyword field vs description indexing, ranking factors)
32- [ ] **App category + seed keywords** — the app's space and starting terms (drives keyword research + scoring)
33- [ ] **Primary goal** — ranking visibility vs conversion rate (shapes metadata, title/subtitle, and screenshot priorities)
34- [ ] **Target market/locale** — which storefronts (drives localization + keyword volume estimates)
35
36Stop 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.
37
38## Quick Start
39
40```bash
41python scripts/keyword_analyzer.py --keywords "todo,task,planner"
42python scripts/metadata_optimizer.py --platform ios --title "App Title"
43python scripts/aso_scorer.py --app-id com.example.app
44```
45
46Note: the scripts are importable Python libraries — see the Tool Reference for classes, methods, and convenience functions.
47
48## References
49
50Load the reference that matches the task — keep this file lean and pull detail on demand:
51
52- **[references/aso-workflows.md](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.
53- **[references/tool-reference.md](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.
54- **[references/operations-and-benchmarks.md](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.
55- **[references/keyword-research-guide.md](references/keyword-research-guide.md)** — research methodology, evaluation framework, and tracking. Read for deep keyword discovery and selection.
56- **[references/platform-requirements.md](references/platform-requirements.md)** — iOS and Android metadata specs and visual asset requirements. Read when validating fields against platform rules.
57- **[references/aso-best-practices.md](references/aso-best-practices.md)** — optimization strategies, rating management, and launch tactics. Read for proven tactics and playbooks.
58
59## Scope & Limitations
60
61**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.
62
63**Out of scope:** real-time store data fetching (scripts analyze static data you provide), Apple Search Ads / Google Ads campaign management, creative asset design, cross-device attribution (use an MMP), in-app analytics/retention, and revenue/subscription pricing.
64
65**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](references/operations-and-benchmarks.md) for details.
66
67## Integration Points
68
69Connects to **Apple App Store Connect** and **Google Play Console** (metadata submission, Product Page Optimization / Store Listing Experiments), **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](references/operations-and-benchmarks.md).
borghei