Skillshub asc-metrics

When the user wants to analyze their own app's actual performance data from App Store Connect — real downloads, revenue, IAP, subscriptions, trials, or country breakdowns synced via Appeeky Connect. Use when the user asks about "my downloads", "my revenue", "how is my app performing", "ASC data", "sales and trends", "my subscription numbers", "App Store Connect metrics", or wants to compare periods or top markets. For third-party app estimates, see app-analytics. For subscription analytics depth, see monetization-strategy.

install

source · Clone the upstream repo

git clone https://github.com/ComeOnOliver/skillshub

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/Eronred/aso-skills/asc-metrics" ~/.claude/skills/comeonoliver-skillshub-asc-metrics && rm -rf "$T"

manifest: skills/Eronred/aso-skills/asc-metrics/SKILL.md

source content

ASC Metrics

You analyze the user's official App Store Connect data synced into Appeeky — exact downloads, revenue, IAP, subscriptions, and trials. This is first-party data, not estimates.

Prerequisites

Appeeky account with ASC connected (Settings → Integrations → App Store Connect)
Indie plan or higher (2 credits per request)
Data syncs nightly; up to 90 days of history available

If ASC is not connected, prompt the user to connect it at appeeky.com/settings and return.

Initial Assessment

Check for
```
app-marketing-context.md
```
— read it for app context
Ask: What do you want to analyze? (downloads, revenue, subscriptions, country breakdown, trend comparison)
Ask: Which time period? (default: last 30 days)
Ask: Specific app or all apps?

Fetching Data

Step 1 — List available apps

GET /v1/connect/metrics/apps

Match the user's app to an

app_apple_id

if not already known.

Step 2 — Get overview (portfolio)

GET /v1/connect/metrics?from=YYYY-MM-DD&to=YYYY-MM-DD

Step 3 — Get app detail (single app)

GET /v1/connect/metrics/apps/:appId?from=YYYY-MM-DD&to=YYYY-MM-DD

Response includes:

daily[]

countries[]

totals

See full API reference: appeeky-connect.md

Analysis Frameworks

Period-over-Period Comparison

Fetch two equal-length windows and compare:

Metric	Prior Period	Current Period	Change
Downloads	[N]	[N]	[+/-X%]
Revenue	$[N]	$[N]	[+/-X%]
Subscriptions	[N]	[N]	[+/-X%]
Trials	[N]	[N]	[+/-X%]
Trial → Sub Rate	[X]%	[X]%	[+/-X pp]

What to look for:

Downloads rising but revenue flat → pricing or paywall issue
Trials rising but conversions flat → paywall or onboarding issue
Revenue rising but downloads flat → good monetization improvement

Daily Trend Analysis

From

daily[]

, identify:

Spikes — Did a feature, update, or press trigger them?
Drops — Correlate with app updates, seasonality, or algorithm changes
Trend direction — 7-day moving average vs prior 7 days

Country Breakdown

Sort

countries[]

by downloads and revenue:

Top 5 by downloads — Are you investing in ASO for these markets?
Top 5 by revenue — Higher ARPD (avg revenue per download) = prioritize ASO
High downloads, low revenue — Markets with weak monetization
Low downloads, high revenue — Under-tapped premium markets (localize)

Revenue Quality Check

Compute from the data:

Metric	Formula	Benchmark
ARPD	Revenue / Downloads	> $0.05 good; > $0.20 excellent
Trial rate	Trials / Downloads	> 20% means strong paywall reach
Sub conversion	Subscriptions / Trials	> 25% is strong
Revenue per sub	Revenue / Subscriptions	Depends on pricing

Output Format

Performance Snapshot

📊 [App Name] — [Period]

Downloads:     [N]  ([+/-X%] vs prior period)
Revenue:       $[N] ([+/-X%])
Subscriptions: [N]  ([+/-X%])
Trials:        [N]  ([+/-X%])
IAP Count:     [N]  ([+/-X%])
Trial→Sub:     [X]%

Top Markets (downloads):
  1. [Country] — [N] downloads, $[N]
  2. [Country] — [N] downloads, $[N]
  3. [Country] — [N] downloads, $[N]

Key Observations:
- [What the trend means]
- [Any anomaly and likely cause]
- [Opportunity identified]

Recommended Actions:
1. [Specific action based on data]
2. [Specific action based on data]

Trend Alert

When a significant change (>20%) is detected, flag it:

⚠️  Downloads dropped [X]% this week
    Possible causes: [list 2-3 hypotheses]
    Next steps: [specific diagnostic actions]

Common Questions

"Why did my downloads drop?"

Pull daily trend — when did it start?
Check if an update shipped on that date
Check keyword rankings (use
```
keyword-research
```
skill)
Check competitor activity (use
```
competitor-analysis
```
skill)

"Which countries should I localize for?" Pull country breakdown → sort by downloads → flag high-download, non-English markets → use

localization

skill

"Is my monetization improving?" Compare trial rate and trial→sub rate period over period → use

monetization-strategy

skill for paywall improvements

Related Skills

```
app-analytics
```
— Full analytics stack setup and KPI framework
```
monetization-strategy
```
— Improve subscription conversion and paywall
```
retention-optimization
```
— Reduce churn using the metrics as input
```
localization
```
— Expand top-performing markets seen in country data
```
ua-campaign
```
— Validate whether paid installs show in downloads spike