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App Analytics

CommunityPopular
Eronred
app-analytics

When the user wants to set up, interpret, or improve their app analytics and tracking. Also use when the user mentions "analytics", "tracking", "metrics", "KPIs", "App Store Connect analytics", "install tracking", "funnel", "attribution", or "how is my app performing". For A/B testing, see ab-test-store-listing. For retention metrics, see retention-optimization.

Overview

PublisherEronred
Repositoryaso-skills
Skill nameapp-analytics
Stars
1.9K
Forks
116
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Eronred on GitHub. Read the source before you install it.

Installation

Install the App Analytics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Eronred/aso-skills.git /tmp/aso-skills
mkdir -p .claude/skills
cp -r /tmp/aso-skills/skills/app-analytics .claude/skills/app-analytics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable App Analytics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads App Analytics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so App Analytics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

App Analytics

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.

Initial Assessment

  1. Check for app-marketing-context.md — read it for context
  2. Ask: What analytics tools do you currently use?
  3. Ask: What are your top 3 questions about your app's performance?
  4. Ask: What decisions do you need data to make?
  5. Ask: Do you run paid acquisition? (attribution matters)

Analytics Stack

Essential Tools

ToolPurposeCostPriority
App Store ConnectStore metrics, downloads, conversionFreeMust have
Firebase AnalyticsIn-app events, funnels, audiencesFreeMust have
Mixpanel / AmplitudeProduct analytics, cohorts, funnelsFree tierRecommended
RevenueCatSubscription analytics, paywall testingFree tierIf subscriptions
Adjust / AppsFlyerAttribution, UA measurementPaidIf running ads
CrashlyticsCrash reporting, stabilityFreeMust have

App Store Connect Analytics

Key metrics available for free:

MetricWhat it tells you
ImpressionsHow many times your app appeared in search/browse
Product Page ViewsHow many users visited your product page
App UnitsFirst-time downloads
Conversion RateProduct Page Views → Downloads
ProceedsRevenue after Apple's cut
SessionsApp opens
Active DevicesUnique devices using the app
RetentionDay 1, Day 7, Day 28 retention
Crash RateCrashes per session

Source types:

  • App Store Search
  • App Store Browse
  • Web Referral
  • App Referral

Key Metrics Framework

Acquisition Metrics

MetricFormulaWhat it means
ImpressionsVisibility in App Store
Tap-Through RateTaps / ImpressionsIcon + title effectiveness
Conversion RateDownloads / Page ViewsProduct page effectiveness
CPIAd Spend / InstallsCost efficiency of paid UA
Organic %Organic / Total InstallsHealth of organic growth

Engagement Metrics

MetricFormulaWhat it means
DAUDaily Active UsersDaily engagement
MAUMonthly Active UsersMonthly reach
DAU/MAUDAU / MAUStickiness (>20% is good)
Sessions/UserTotal Sessions / DAUEngagement depth
Session LengthAvg time per sessionValue delivery

Retention Metrics

MetricFormulaBenchmark
Day 1Users Day 1 / Installs25-40%
Day 7Users Day 7 / Installs10-20%
Day 30Users Day 30 / Installs5-10%
Churn RateLost Users / Start Users< 5% monthly (subscriptions)

Revenue Metrics

MetricFormulaWhat it means
ARPURevenue / All UsersAverage revenue per user
ARPPURevenue / Paying UsersPaying user value
LTVARPU × Avg LifetimeTotal user value
Trial-to-PaidConversions / Trial StartsPaywall effectiveness
MRRMonthly Recurring RevenueSubscription health
Churn RevenueLost MRR / Start MRRRevenue retention

Event Tracking Plan

Core Events (track these minimum)

# 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)

Event Naming Conventions

  • Use snake_case
  • Format: [object]_[action] (e.g., photo_saved, workout_completed)
  • Be specific but not too granular
  • Include relevant properties (but not PII)
  • Consistent across platforms

Dashboard Setup

Executive Dashboard (check weekly)

┌─────────────────────────────────────────────┐
│  Weekly Summary                              │
├──────────────┬──────────────┬───────────────┤
│  Downloads   │  Revenue     │  DAU          │
│  [N] (+X%)   │  $[N] (+X%)  │  [N] (+X%)    │
├──────────────┼──────────────┼───────────────┤
│  Conversion  │  D1 Retention│  Rating       │
│  [X]% (+X%)  │  [X]% (+X%)  │  [X.X] ★      │
└──────────────┴──────────────┴───────────────┘

Funnel Dashboard (check daily)

Impressions → Page Views → Downloads → Activation → Purchase
   [N]          [N]          [N]          [N]          [N]
        [X]%         [X]%         [X]%          [X]%

Cohort Dashboard (check monthly)

Retention curves by:

  • Install date cohort
  • Acquisition source
  • Country
  • Subscription plan

Output Format

Analytics Audit

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]

Tracking Plan

Provide a complete event tracking plan with:

  • Event name
  • When it fires
  • Properties to include
  • Which tool tracks it

Metric Interpretation

When the user shares data, provide:

  • How their metrics compare to benchmarks
  • What the trends indicate
  • Specific actions to take based on the data

Related Skills

  • ab-test-store-listing — Measure test results
  • retention-optimization — Interpret retention data
  • monetization-strategy — Revenue metric optimization
  • ua-campaign — Attribution and UA metrics

Frequently asked questions

What does the App Analytics AI skill do?

When the user wants to set up, interpret, or improve their app analytics and tracking. Also use when the user mentions "analytics", "tracking", "metrics", "KPIs", "App Store Connect analytics", "install tracking", "funnel", "attribution", or "how is my app performing". For A/B testing, see ab-test-store-listing. For retention metrics, see retention-optimization.

Why use App Analytics on TypingMind?

Because you install it once and use it with any model. App Analytics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install App Analytics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Eronred/aso-skills/tree/main/skills/app-analytics. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use App Analytics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with App Analytics?

As many as you like. As long as a model supports skills, you can use App Analytics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the App Analytics AI skill free?

Yes. It is published on GitHub by Eronred under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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