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ads-attribution

该 Skill 以六步流程审计跨平台广告归因:读取广告合约与标准化账户快照,声明业务转化、价值、数据窗口、时区、币种与决策上下文,清点浏览器、服务器、平台、分析、MMP、离线与应用等归因来源及其计数去重规则,核对可比事件并解释差异,区分度量质量与平台报告表现,最后通过统一 JSON 合约输出发现、矛盾、置信度、缺失证据与度量改进计划。

AgriciDaniel
热度 待计算
最近更新 2026-10-19 00:59:25
分类:营销依赖:纯提示词应用场景:市场分析 / 报告撰写

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add agricidaniel/claude-ads/ads-attribution
Metadata
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.