Install
$ agentstack add skill-eronred-aso-skills-asc-metrics ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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](../../tools/integrations/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
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Eronred
- Source: Eronred/aso-skills
- License: MIT
- Homepage: https://appeeky.com/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.