About ads-attribution
ads-attribution is a marketing skill for auditing attribution measurement across advertising and analytics sources. It reconciles differences in definitions and data scope, and prevents aggregation until sources are comparable.
Key features
- 01Inventory attribution sources and rules
- 02Reconcile comparable conversion events
- 03Separate measurement quality from performance
- 04Report contradictions and confidence
- 05Block aggregation of incompatible metrics
- 06Return findings and improvement plans as JSON
Use cases
- 01Audit cross-platform conversion reporting
- 02Investigate discrepancies between attribution sources
- 03Assess evidence before aggregating reported conversions
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/ads-attributionin Claude Code to confirm it loaded.
Details
From the project READMEAttribution Audit
- Read the main
adscontract and normalized account snapshots. - Declare the business conversion, value, data window, timezone, currency, and decision the attribution analysis must support.
- Inventory every browser, server, platform, analytics, MMP, offline, and app attribution source with its identity, counting, deduplication, and privacy rules.
- Reconcile comparable events and explain differences caused by eligibility, view-through rules, consent, modeled data, conversion lag, thresholds, or scope.
- Separate measurement quality from platform-reported performance.
- 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.