App Store Optimization

# Create a "App Store Optimization" AI Skill

I want you to help me set up a reusable AI skill that I can use in future conversations. Read the complete skill definition below, then help me install it.

## Complete Skill Definition

# 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

```bash
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](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](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](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](references/keyword-research-guide.md)** — research methodology, evaluation framework, and tracking. Read for deep keyword discovery and selection.
- **[references/platform-requirements.md](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](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](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](references/operations-and-benchmarks.md).

---

## What I Need You to Do

First, detect which platform I'm using (Claude.ai, ChatGPT, etc.) and follow the matching instructions below.

### If I'm on Claude.ai:

Walk me through these exact steps:

1. **Create the Project:** Tell me to go to **claude.ai > Projects > Create project** and name it **"App Store Optimization"**

2. **Add Project Knowledge:** Give me the COMPLETE skill definition above as a single copyable text block inside a code fence. Tell me to click **"Add content" > "Add text content"** inside the project, then paste that entire block. Do NOT say "paste from above" -- give me the actual text to copy right there.

3. **Set Custom Instructions:** Tell me to open project settings and paste this exact instruction:
   "You are an expert App Store Optimization in the Marketing domain. Use the project knowledge as your expertise. Follow the workflows, frameworks, and templates defined there. Always provide specific, actionable output."

4. **Test It:** Give me a specific sample prompt I can use inside the new project to verify it works. Pick a real task from the skill's workflows.

### If I'm on ChatGPT:

Walk me through these exact steps:

1. **Create a Custom GPT:** Tell me to go to **chatgpt.com > Explore GPTs > Create**
2. **Configure it:**
   - Name: **"App Store Optimization"**
   - Description: "App Store Optimization toolkit for researching keywords, optimizing metadata, and tracking mobile app performance on Apple App Store and Google Play Store."
   - Instructions: Give me the COMPLETE skill definition above as a single copyable text block inside a code fence to paste into the Instructions field. Do NOT say "paste from above."
3. **Test It:** Give me a sample prompt to verify it works.

### If I'm on another platform:
Ask which tool I'm using and adapt the instructions accordingly.

## Important
- Always provide the full skill text in a ready-to-copy code block -- never tell me to "scroll up" or "copy from above"
- Keep the setup steps simple and numbered
- After setup, test it with me using a real workflow from the skill

Source: https://github.com/borghei/Claude-Skills/tree/main/marketing/app-store-optimization/SKILL.md