| name | attribution-reconciler |
| slug | aaron-attribution-reconciler |
| displayName | Attribution Reconciler · 付费广告归因对账 |
| summary | 付费广告归因对账/去重/增量 |
| description | Use when platform-reported conversions disagree with GA4/ecommerce, when you suspect Meta and Google are double-counting the same sales, or for a standing (monthly) reconciliation workbook that de-dups stacked credit against an order-ID truth set, normalizes attribution windows and currency, compares attribution models, and reads incrementality from a geo/holdout test. Not for the point-in-time R2 veto or RQS gate — use ad-account-auditor; not for the ROI/ROAS ratio math itself — use roi-calculator; not for organic dark-social share attribution or GA4 direct-traffic decomposition — use dark-social-attributor. 付费广告归因对账/去重/增量 |
| version | 19.2.0 |
| license | Apache-2.0 |
| compatibility | Claude Code and compatible agent-skill hosts |
| homepage | https://github.com/aaron-he-zhu/aaron-marketing-skills |
| when_to_use | Use when running a standing reconciliation of platform-reported conversions against the GA4/ecommerce order-ID truth set: de-dup stacked credit across Meta + Google, normalize differing attribution windows and currency, compare attribution models side by side, and read incrementality where a geo/holdout test exists. Activate when the user has each platform's conversion export plus an order-ID export and wants to know which conversions are real and not double-counted. |
| argument-hint | <GA4/ecommerce order-ID export> [platform conversion exports] [goal: DR|prospecting] |
| metadata | {"author":"aaron-he-zhu","version":"19.2.0","discipline":"ad","phase":"scale","geo-relevance":"low","hermes":{"tags":["marketing","ad","scale"],"category":"ad"},"openclaw":{"emoji":"🎯","homepage":"https://github.com/aaron-he-zhu/aaron-marketing-skills"}} |
Attribution Reconciler
Based on the ROAS dimension R (attribution integrity) in the ROAS Benchmark. This is the standing de-dup / incrementality workbook: it reconciles platform-reported conversions against the GA4/ecommerce order-ID truth set on a recurring cadence. It delegates all ratio/ROAS math to roi-calculator and does not re-run the R2 veto — ad-account-auditor judges R2 once, point-in-time. This workbook just keeps the truth set clean between audits. Upstream, conversion-signal-qa is the pre-launch instrumentation pass that makes the signal trustworthy and only gates that a dedup rule exists; this skill is the recurring reconciliation that runs on that signal — match, de-dup, quantify, read incrementality.
The single rule: the truth set is the order IDs from GA4/ecommerce, never any platform's reported-conversion count. This workbook reconciles paid channels only — decomposing GA4 direct traffic and estimating organic dark-social share attribution belongs to dark-social-attributor.
Quick Start
Reconcile my paid conversions for May. Truth set is this GA4 order-ID export. Here are the Meta and Google conversion exports. Find the double-counting.
Build the monthly attribution workbook: normalize Meta's 7-day-click window and Google's 30-day window to a common window, convert currencies, then show de-duped conversions per platform against my Shopify order export.
I ran a geo holdout for two weeks. Here's the test-region and control-region order export plus the platform spend. Read the incrementality and compare it to last-click.
Skill Contract
- Expected output: a reconciliation workbook that maps every platform-reported conversion to (or away from) an order in the truth set, a de-duped conversion count per platform, a normalized-window/currency view, an attribution-model comparison table, and an incrementality read if a holdout exists.
- Reads: the GA4/ecommerce order-ID export (truth set), each platform's (reported conversions with claimed order IDs/timestamps/windows), the stated attribution window per platform, currency per export, and any geo/holdout test export (test vs control orders + spend). The ROAS profile () is context only.
