pinkpixel-dev/asc-metrics
SKILLS/asc-metrics/SKILL.md
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 --global
skillsauthnpx skillsauth add pinkpixel-dev/skills-collection-1 asc-metricsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- asc-metrics
- description:
- 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.
- version:
- 1.0.0
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-researchskill) - Check competitor activity (use
competitor-analysisskill)
"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 frameworkmonetization-strategy— Improve subscription conversion and paywallretention-optimization— Reduce churn using the metrics as inputlocalization— Expand top-performing markets seen in country dataua-campaign— Validate whether paid installs show in downloads spike
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