Ad Attribution
Privacy-preserving ad measurement with AdAttributionKit (SKAdNetwork's successor), ...
Install
npx skills add rshankras/claude-code-apple-skills --skill ad-attribution -g -a claude-code -y- Repository
- rshankras/claude-code-apple-skills846
- Category
- Design
- Updated
- 3mo ago
- License
- MIT
- Context cost
- Lightabout 1,375 tokens
- Runs scripts
- No, instructions only
What it does
Privacy-preserving ad measurement with AdAttributionKit (SKAdNetwork's successor), install and re-engagement attribution, conversion-value strategy under crowd anonymity, and end-to-end postback testing. Use when running paid acquisition beyond Apple Ads, measuring re-engagement campaigns, designing conversion values, or migrating from SKAdNetwork.
Ad Attribution (AdAttributionKit)
You can't optimize paid traffic you can't measure. AdAttributionKit is Apple's privacy-preserving attribution framework, interoperable with SKAdNetwork, and the go-forward path (it also works in alternative marketplaces). This skill covers the advertised-app side: what an indie running paid campaigns must implement and how to design conversion signals that survive privacy thresholds.
Note the seam: Apple Ads attribution uses the AdServices framework, not AdAttributionKit, campaigns there are measured in the Apple Ads console (see app-store/apple-search-ads). This
skill is for every other paid channel (ad networks, social, publishers).
When This Skill Activates
- Running or planning paid user acquisition outside Apple Ads
- "How do I know which campaign drove installs?" / measuring re-engagement ads
- Designing or debugging conversion values / postbacks
- Migrating from SKAdNetwork (fully interoperable, registered networks need no re-enrollment)
How it works (the 60-second model)
Ad network signs an ad (compact JWS impression) → publisher app shows it → user installs (or re-engages) → after a privacy-delayed measurement window, a postback with your conversion info goes to the ad network (and optionally to you). Attribution requires no user tracking; the price is coarser data, governed by crowd anonymity.
