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

ads-attribution

Skill10k starsChanged 17 days agoMITView on GitHub

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.

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read the main ads contract and normalized account snapshots.

    line 8
  2. Distinctive:

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

    line 19

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
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.

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Trending

More talk lately.thin dataabout the whole repository

Dec 2025: 0 mentionsJan 2026: 0 mentionsFeb 2026: 2 mentionsMar 2026: 3 mentionsApr 2026: 2 mentionsMay 2026: 1 mentionJun 2026: 0 mentionsJul 2026: 0 mentionsAug 2026: 0 mentionsSep 2026: 1 mentionOct 2026: 5 mentionsNov 2026: 0 mentions
Dec 2025Jun 2026Nov 2026
Mentions a month of the AgriciDaniel/claude-ads repository that our weekly crawl found: only 14 in all, so this is a hint, not a trend. There is no adoption data for this file yet.

Who's talking about AgriciDaniel/claude-ads

The biggest 5 of 14 mentions linking to the repository that our crawl found. Each line is their own headline.

See all 14

Talked about on GitHub and the web (14 times).

On GitHub · 11

6 more on GitHub

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