Cross-platform attribution health audit

01What is it?
Cross-platform attribution health audit covering AdAttributionKit (iOS view-through 24h post-impression, WWDC 2025 configurable windows), GA4 attribution models (data-driven vs last-click), Consent Mode V2 enforcement, server-side attribution stitching, MMP integration health, and cross-device / cross-platform attribution. It stands out by giving conversion optimization a defined shape, so the agent asks for better context and returns a more usable result.
02Inputs
Context for conversion optimization: your goals, audience, constraints, and any source material the skill asks for.
03Output
A ready-to-use result for conversion optimization: the analysis, copy, or recommendations the agent produces.
Paste-ready

Install as a package

Installs this one skill package for your coding agent, including any supporting files that skill ships with — not every skill in the repository. Read the tutorial.

Terminal
$ npx skills add agricidaniel/claude-ads --skill ads-attribution

Use in Profound

Copy this file into a new Profound Skill. That's it, nothing else to install.

Copy and create in Profound
SKILL.md

Attribution Audit

  1. Read the main ads contract and normalized account snapshots.
  2. Declare the business conversion, value, data window, timezone, currency, and decision the attribution analysis must support.
  3. Inventory every browser, server, platform, analytics, MMP, offline, and app attribution source with its identity, counting, deduplication, and privacy rules.
  4. Reconcile comparable events and explain differences caused by eligibility, view-through rules, consent, modeled data, conversion lag, thresholds, or scope.
  5. Separate measurement quality from platform-reported performance.
  6. Return findings, contradictions, confidence, missing evidence, and a measurement improvement plan through the common JSON contract.

Do not assume one platform is ground truth, add incompatible reports together, or recommend an attribution model without the operator's decision context.

Comparability gate

Reject aggregation until the sources share, or are explicitly normalized to, the same conversion event and value definition, attribution window, click/view scope, counting method, deduplication identity, timezone, currency, attribution model, and modeled-data treatment. Until then, report the values side by side with their definitions; do not compute a total.

Example: Meta seven-day conversions and Google thirty-day conversions are incompatible. Refuse to add them, reconcile windows and definitions first, and only aggregate a newly comparable dataset.

How do I install Cross-platform attribution health audit in Cursor, Claude Code, or Codex?

Run npx skills add agricidaniel/claude-ads --skill ads-attribution in the project where you want it, then ask your agent for the skill by name. The --skill flag installs only Cross-platform attribution health audit, not every skill in the repository.

Where does Cross-platform attribution health audit come from and what license is it under?

Cross-platform attribution health audit comes from the agricidaniel/claude-ads repository on GitHub. That repository has 6.8K GitHub stars. The skill is published under the MIT license.

Prefer plain text? Read the Cross-platform attribution health audit guide as markdown.