Asc Metrics

Pulls your actual App Store Connect data through Appeeky Connect and turns it into actionable analysis. When you ask "how's my app doing" or "why did downloads drop last week," it fetches real downloads, revenue, subscriptions, trials, and country breakdowns, then runs period over period comparisons and spots trends. The framework is solid: it flags spikes and drops, calculates ARPD and conversion rates, and identifies which markets are converting well versus just downloading. Requires an Indie plan and ASC integration. Most useful when you need to diagnose a sudden change or decide which countries to localize for based on actual performance, not guesses.

skills.sh installs: 1.7K
GitHub stars: 1.7K
last updated: Aug 21, 2026

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Install

$ npx -y skills add eronred/aso-skills --skill asc-metrics --agent claude-code

Installs into .claude/skills of the current project.

Files

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

SKILL.md on GitHub

Details

Repository: github.com/eronred/aso-skills
Categories: Data Science & ML, Debugging, Marketing & SEO, Sales & Marketing
First seen: May 16, 2026