ads-attribution
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.
- License 许可
- MIT
- 最近更新
- 2026-10-01
- Stars
- ★ 1
- Forks
- 0
01 前言 Front Matter
- 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.
02 资源文件 2
- 许可证LICENSE1.0 KB
- 参考文档UPSTREAM.md801 B
03 SKILL.md 正文
Attribution 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.
04 同仓库更多 Skills
template
YOUR_DESCRIPTION_HERE (shown in command palette, keep under 80 chars)
multi
Line one continues here. Second sentence.
ffmpeg
FFmpeg recipes for video production.
secret-skill
Private, never published.
external-no-license
Has no license.
write-swift
How to write modern Swift well — modeling with value types, Swift 6 data-race safety and approachable concurrency (@concurrent, main-actor-by-default, actors, task groups), protocols and generics (some vs any), API design, performance and ARC, Swift Testing, macros, and the modern language features agents don't know about yet. Use when writing, reviewing, or migrating Swift, or when a concurrency error, a hang, a data race, a retain cycle, or a performance problem needs fixing.