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ads-attribution — Claude Code skill: attribution audit

Audits advertising attribution across platforms and blocks totals built from incompatible conversion reports. Use it when GA4, MMP, or platform figures disagree.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓ 
npx skills add AgriciDaniel/claude-ads --skill ads-attribution -a claude-code
Open on GitHub

What ads-attribution does

The skill reads the ads contract and normalized account snapshots, then defines the business conversion, value, data window, timezone, currency, and decision the analysis must support. It inventories browser, server, platform, analytics, MMP, offline, and app attribution sources.

It reconciles comparable events and explains gaps caused by eligibility, view-through rules, consent, modeled data, conversion lag, thresholds, or scope. The result includes findings, contradictions, confidence, missing evidence, and a measurement improvement plan through a shared JSON contract.

It will not total reports from different windows. Meta seven-day conversions and Google thirty-day conversions remain separate until the event, value, window, click and view scope, counting, deduplication, timezone, currency, model, and modeled-data treatment align. That is a reason to check reporting rules before using a combined figure.

What it helps with

  • reconcile Meta and Google conversion figures
  • trace discrepancies between GA4 and platform reports
  • audit browser and server event deduplication
  • review AppsFlyer, Adjust, Branch, or Singular attribution
  • separate measurement quality from platform-reported performance

Example prompts for the agent

  • "Audit attribution across Meta, Google, and GA4; check conversion definitions and reporting windows."
  • "Compare browser and server events, then identify deduplication rules and discrepancies."
  • "Can these Meta and Google conversions be added together? Check window and attribution-model compatibility first."

How to install ads-attribution

1Skills CLI (Vercel)

npx skills add AgriciDaniel/claude-ads --skill ads-attribution -a claude-code

2Manually: put the files in place

~/.claude/skills/ads-attribution/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/ads-attribution/

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Questions about ads-attribution

How do I install ads-attribution in Claude Code?

Run npx skills add AgriciDaniel/claude-ads --skill ads-attribution -a claude-code, then run npx skills add AgriciDaniel/claude-ads --skill ads-attribution -a claude-code again. The skill file is at ~/.claude/skills/ads-attribution/SKILL.md.

What does ads-attribution do?

It audits attribution across advertising platforms, analytics, MMPs, and offline sources. It checks conversion definitions, reporting windows, counting, and deduplication rules.

Why can’t Meta and Google conversions be added together?

The figures need a shared conversion event, attribution window, counting rules, and other comparability conditions before aggregation. Meta seven-day and Google thirty-day conversions are treated as incompatible.

Source text

SKILL.md · MIT 
---
name: ads-attribution
description: "Audit cross-platform attribution, conversion definitions, reporting windows, GA4, AdServices and AdAttributionKit, MMPs, browser and server events, offline conversions, and platform reconciliation. Use for attribution audit, attribution models, conversion windows, requests to add or total Meta and Google conversions, incompatible reporting-window aggregation, GA4 attribution, MMP review, AppsFlyer, Adjust, Branch, Singular, or cross-platform discrepancies."
---

# 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.

© AgriciDaniel · MIT · AgriciDaniel/claude-ads

See also

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