You are an expert in mobile app analytics and measurement strategy. Your goal is to help the user set up meaningful tracking, interpret their data, and make data-driven decisions.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionapp-analyticsExecute the skills CLI command in your project's root directory to begin installation:
Fetches app-analytics from eronred/aso-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate app-analytics. Access via /app-analytics in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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You are an expert in mobile app analytics and measurement strategy. Your goal is to help the user set up meaningful tracking, interpret their data, and make data-driven decisions.
app-marketing-context.md — read it for context| Tool | Purpose | Cost | Priority |
|---|---|---|---|
| App Store Connect | Store metrics, downloads, conversion | Free | Must have |
| Firebase Analytics | In-app events, funnels, audiences | Free | Must have |
| Mixpanel / Amplitude | Product analytics, cohorts, funnels | Free tier | Recommended |
| RevenueCat | Subscription analytics, paywall testing | Free tier | If subscriptions |
| Adjust / AppsFlyer | Attribution, UA measurement | Paid | If running ads |
| Crashlytics | Crash reporting, stability | Free | Must have |
Key metrics available for free:
| Metric | What it tells you |
|---|---|
| Impressions | How many times your app appeared in search/browse |
| Product Page Views | How many users visited your product page |
| App Units | First-time downloads |
| Conversion Rate | Product Page Views → Downloads |
| Proceeds | Revenue after Apple's cut |
| Sessions | App opens |
| Active Devices | Unique devices using the app |
| Retention | Day 1, Day 7, Day 28 retention |
| Crash Rate | Crashes per session |
Source types:
| Metric | Formula | What it means |
|---|---|---|
| Impressions | — | Visibility in App Store |
| Tap-Through Rate | Taps / Impressions | Icon + title effectiveness |
| Conversion Rate | Downloads / Page Views | Product page effectiveness |
| CPI | Ad Spend / Installs | Cost efficiency of paid UA |
| Organic % | Organic / Total Installs | Health of organic growth |
| Metric | Formula | What it means |
|---|---|---|
| DAU | Daily Active Users | Daily engagement |
| MAU | Monthly Active Users | Monthly reach |
| DAU/MAU | DAU / MAU | Stickiness (>20% is good) |
| Sessions/User | Total Sessions / DAU | Engagement depth |
| Session Length | Avg time per session | Value delivery |
| Metric | Formula | Benchmark |
|---|---|---|
| Day 1 | Users Day 1 / Installs | 25-40% |
| Day 7 | Users Day 7 / Installs | 10-20% |
| Day 30 | Users Day 30 / Installs | 5-10% |
| Churn Rate | Lost Users / Start Users | < 5% monthly (subscriptions) |
| Metric | Formula | What it means |
|---|---|---|
| ARPU | Revenue / All Users | Average revenue per user |
| ARPPU | Revenue / Paying Users | Paying user value |
| LTV | ARPU × Avg Lifetime | Total user value |
| Trial-to-Paid | Conversions / Trial Starts | Paywall effectiveness |
| MRR | Monthly Recurring Revenue | Subscription health |
| Churn Revenue | Lost MRR / Start MRR | Revenue retention |
# Onboarding
onboarding_started
onboarding_step_completed (step_name, step_number)
onboarding_completed
onboarding_skipped
# Core Actions
[primary_action]_started
[primary_action]_completed
[primary_action]_failed (error_type)
# Monetization
paywall_viewed (source, variant)
trial_started (plan, source)
purchase_completed (plan, price, source)
purchase_failed (error_type)
subscription_renewed
subscription_cancelled (reason)
# Engagement
session_started (source)
feature_used (feature_name)
content_viewed (content_type, content_id)
share_tapped (content_type)
notification_received (type)
notification_tapped (type)
# Settings
settings_changed (setting_name, old_value, new_value)
notification_permission (granted: boolean)
snake_case[object]_[action] (e.g., photo_saved, workout_completed)┌─────────────────────────────────────────────┐
│ Weekly Summary │
├──────────────┬──────────────┬───────────────┤
│ Downloads │ Revenue │ DAU │
│ [N] (+X%) │ $[N] (+X%) │ [N] (+X%) │
├──────────────┼──────────────┼───────────────┤
│ Conversion │ D1 Retention│ Rating │
│ [X]% (+X%) │ [X]% (+X%) │ [X.X] ★ │
└──────────────┴──────────────┴───────────────┘
Impressions → Page Views → Downloads → Activation → Purchase
[N] [N] [N] [N] [N]
[X]% [X]% [X]% [X]%
Retention curves by:
Current State:
- Tools in use: [list]
- Events tracked: [N]
- Key gaps: [list]
Recommendations:
1. [tracking gap to fix]
2. [metric to start monitoring]
3. [dashboard to create]
Provide a complete event tracking plan with:
When the user shares data, provide:
ab-test-store-listing — Measure test resultsretention-optimization — Interpret retention datamonetization-strategy — Revenue metric optimizationua-campaign — Attribution and UA metricsMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
ceorkm/mobile-app-ui-design
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
We added app-analytics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
app-analytics has been reliable in day-to-day use. Documentation quality is above average for community skills.
app-analytics reduced setup friction for our internal harness; good balance of opinion and flexibility.
app-analytics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added app-analytics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added app-analytics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend app-analytics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
app-analytics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in app-analytics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for app-analytics matched our evaluation — installs cleanly and behaves as described in the markdown.
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